Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data

GRD Journals- Global Research and Development Journal for Engineering | Volume 4 | Issue 11 | October 2019 ISSN: 2455-5703

Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data Dr. Kurikkil Veettil Yooseff Ibrahim Visiting Faculty Department of Applied Science and Engineering Jai Bharath College of Management & Engineering Technology

Abstract The utilization of computers in any field by using deep learning (DL) allows task performance with high efficiency and least human errors (1-2). This is possible as most of the tasks are completed by robotic computers with minimal participation of humans.

Moreover, artificial intelligence has been one of the most influential information technology revolutions in health science as these robotic operations have immense importance and application in medicine science. Further, the application of artificial intelligence in ophthalmology mainly focused on the diseases like diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract and few with retinal vein occlusion. Artificial intelligence mainly uses machine learning or DL that has ability to process input data without the need for manual feature engineering. In this way, completely automated systems recognise the intricate structures in high-dimensional data through projection onto a lower dimensional manifold. So, AI allows higher accuracies in many domains, including natural language processing, computer vision and voice recognition in comparison to conventional techniques. The present study is focused to evaluate potential of artificial intelligence (AI) in ophthalmology by concentrating of important ophthalmological diseases.

Keywords- Artificial Intelligence (AI), Deep Learning (DL), Imaging Technology, Artificial Intelligence Ophthalmology

I. INTRODUCTION Artificial Intelligence (AI) is known as the fourth industrial revolution in the history of mankind (World Economic Forum, 2016).

In order to make focus on deep learning, it is known as a class of sate of the art machine language, a technique that has adopted tremendously in the last few years (LeCun, Bengio & Hinton, 2015). It is also known as representing a learning method that includes multiple levels of abstraction to process into data without any help for manual features of engineering. It is automatically recognized by the intricate structure that has high dimensional data with the help of projection on to lower-dimensional (2). To make comparison with other conventional techniques, it has been shown that deep learning (DL) is used to achieve high accuracies in different domains such as natural language processing, computer vision and recognition of voice (Hinton, Deng, Yu, Dahl, Mohamed, Jaitly & Sainath, 2012).

In the field of medicine and health care sector, DL mainly applied as medical imaging analysis that has shown robotic diagnostic performance in order to detect several medical conditions such as tuberculosis from chest x-ray, malignant, melanoma, all skin photograph, lymph node metastate to identify the breast cancer from tissue section (Bejnordi, Veta, Van Diest, Van Ginneken, Karssemeijer, Ltjens & Geessink, 2017). Hence, it can be said that DL has been applied to ocular imaging and optical coherence tomography. With the help of DL techniques, it mainly uses to identify major ophthalmic diseases such as diabetic, retinopathy, glaucoma, muscular degeneration and retinopathy of prematurity (Brown, Campbell, Beers, Chang, Ostmo, Chan & Chiang, 2018). It also used to estimate refractive error and identify cardiovascular risk such as blood pressure, the status of smoking and body mass index (BMI) (Varadarajan, Poplin, Blumer, Angermueller, Ledsam, Chopra & Webster, 2018).

To identify the major benefit if DL, in ophthalmology mainly include screening for DR and ROP. All these screening require skilled manpower and financial resources from the health care system because it is known as a long-term process to screen and monitor the patient within the primary eye care setting (Poplin, Varadarajan, Blumer, Liu, McConnell, Corrado & Webster, 2018).

II. RESEARCH AIMS AND OBJECTIVES The major aim of the study is to investigate a diagnosis of eye diseases using imaging ophthalmic data. The other objectives are as follows:  To examine a general framework for standardized recording to patient symptoms and clinical observation by keeping exponential development of medical knowledge from clinical trial and medical advancement  To make the importance of machine learning or AI to analyze a large number of features that are required for diagnosis more effectively than humans.  To make focus on the DL application of ophthalmology and its uses in different fields.

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Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data (GRDJE/ Volume 4 / Issue 11 / 002)

III. RESEARCH QUESTIONS   

Why is it necessary to examine a general framework for standardized recording to patient symptoms and clinical observation by keeping the exponential development of medical knowledge from clinical trial and medical advancement?

What is the importance of machine learning or AI to analyze a large number of features that are required for diagnosis more effectively than humans?

How DL applications of ophthalmology are beneficial and identify its uses in a different field?

IV. LITERATURE REVIEW DL applications in ophthalmology A. Diabetic Retinopathy According to Yau, Rogers, Kawasaki, Lamoureux, Kowalski, Bek & Haffner, (2012) globally there are 600 million people who will have diabetes by 2040 and by conducting a food analysis it was found that 22896 people are suffering from diabetes from 35 pollution based studies in Australia, Europe, and the USA. The screening with the help of DL known as a timely and effective treatment and also known as a strategy that is adopted universally for preventing blindness. Hutchinson, McIntosh, Peters, O'keeffe, Khunti, Baker & Booth, (2000) examined that DL screening can be performed by different health care practitioners like ophthalmologists, optometrists, general practitioners, clinical photographer, and screening technician. Hence all these screening methods mainly include direct Oththalmoscopy, mydriatic or non-mydriatic photography, tele-retinal screening, and retinal video recording. Hence, it can be said that the DL screening learning program is known as the biggest issue that is related to human assessors and the availability of long term financial sustainability.

According to Gargeya & Leng, (2017) over the past few years, there are many revolutionary changes found in DL diagnostic performance that focus on detecting DR by using techniques that involve many groups that have shown excellent performance of diagnostic. According to Gulshan, Peng, Coram, Stumpe, Wu, Narayanaswamy & Kim, (2016) the DL system is developed by using 128-175 retinal images that graded between 3 and 7 times for DR and DMO by taking 54 US licensed ophthalmologists and ophthalmology residents between the year 2005 May and December.

Abràmoff, Lavin, Birch, Shah & Folk, (2018) examined that to get a good result with the help of the DL system on the available data set publicly, it was found that DL was not tested in the real-world by then help of the DR screening program.

B. Age-related Macular Degeneration According to Wong, Su, Li, Cheung, Klein, Cheng & Wong, (2014) age-related macular degeneration (AMD) is known as the major cause of vision impairment, especially in the elderly population. It is classified into now, early, intermediate and late agerelated macular degeneration state, the people with intermediate AMD should be at least seen once every two years and it is projected that there will 288 million patients who have suffered from this kind of stage by the year 2014.

According to Ting, Pasquale, Peng, Campbell, Lee, Raman & Wong, (2019) to diagnose age-related macular degeneration, it is clinically acceptable that DL is suitable for detecting the AMD stage inpatient because the DL system was trained and tested by using 108-558 retinal images from 38-189 patients. In the age-related macular degeneration data set DL system was developed by using the high number of referable AMD such intermediate AMD or worse.

C. DM, Choroidal Neovascularisation and other Macular Diseases According to Centers for Medicare & Medicaid Services, (2016) OCT is known as the transformative impact that is observed in the macular disease management system. It also includes neovascular AMD and DMO features and provides a microscopic view of the retina. Along with the minute view of the retina in vivo, it also provides information related to protocols related to the structural detail of the retina which is not provided by any other medically operated machinery or ophthalmic examination techniques. As a result, a significant rise has been recorded in the segment of macular OCTs and reached to 6.4 million in the year 2016 from 4.3 million in the year 2012.

Ting, Pasquale, Peng, Campbell, Lee, Raman & Wong, (2019) examined that macular OCTs are also known to provide accurate information related to DL modalities. It works in phases, in which in the first phase, it collects information from all around the world. In order to collect data on such a large basis in a routine manner, several OCTs are trained with a DL system so that the training examples will help in the convergence of multiple layered networks effectively. In the second phase, the macular OCTs, three-dimensional structural information is captured including all the real-world images, colored pictures so that consistency is maintained in the viewing of the macula and the foveal fixation. In the third phase, the macular OCTs provide thorough information about the structural insights that are not provided by the traditional imaging methods. It also provides intricate learning about the novel biomarkers of the disease that are not identified by any other machine.

According to Lee, Baughman & Lee, (2017), the early usage of macular OCTs concerning DL was executed with the help of automated classification procedures of AMD. As a result, to get more accurate outcomes 100,000 OCT B-scans operators were trained as DL classifiers that were specifically based on VGG specification of 16 to gain the AUC value of 0.97. (33) Examined

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Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data (GRDJE/ Volume 4 / Issue 11 / 002)

that a new technique called transfer learning was used to provide training the OCT B-scans operators by using neural networks and ImageNet so that the learners will be able to make retinal disease classification.

Ronneberger, Fischer & Brox, (2015) examined that DL also highly laid a transformative influence on the segmentation of the neural networks and developed a semantic segregation process by using U-Net. (13) analyzed that to carry out the OCT Bscans, there was segmentation of intraretinal fluid cysts. It also included the subretinal fluid and moved beyond the conventional methods of segregation of retinal anatomical margins. According to Prentašić, Heisler, Mammo, Lee, Merkur, Navajas & Lončarić, (2016) the similar approaches were also adopted in the segmentation of OCTA images so that the foveal avascular area understated efficiently.

De Fauw, Ledsam, Romera-Paredes, Nikolov, Tomasev, Blackwell & van den Driessche, (2018) examined that the medical healthcare centers like Moorfields Eye Hospital and DeepMind have used the advanced neural networks to segregate and categorize the DL. As a result, AI is used efficiently to segment the network and describe the different ranges of the retinal morphological aspects. It also includes the 15 different morphological phases of retina and OCT related facts. Moreover, the referral triage decision has been further classified into four categories which are based on urgency, semi-urgency, schedule, and surveillance. The other 10 classifications that belong to the OCT pathologies like a full-thickness macular hole, choroidal neovascularisation (CNV), drusen, geographic atrophy are included. It also incorporates (CNV), macular oedema without CNV, central serous retinopathy, normal, and partial thickness macular hole.

According to Buchan, Amoaku, Barnes, Cassels-Brown, Chang, Harcourt & MacEwen, (2017) the Moorfields-DeepMind system is also highly beneficial for recognizing the performance of different classified OCT pathologies. The analyzing of the performance eat par was executed by using classification methods in all the settings including retrospective settings. This method may be used for segmentation of network in the entire OCT systems with the help of several vendors and restrictive methods that are included in the retraining purposes also. As a result, it was determined that the DL system is effectively implemented in the analysis of the real-world clinical diagnosis process and identifiers the triaging of macular disease in countries like the United Kingdom. Kern, Fu, Kortuem, Huemer, Barker, Davis & Sim, (2019) also analyzed that OCT systems helps in the conducting triaging process of the patients and is increasingly adopted by the health care practitioners and optometrists in all the communities.

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Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data (GRDJE/ Volume 4 / Issue 11 / 002)

D. Glaucoma According to Tham, Li, Wong, Quigley, Aung & Cheng, (2014) glaucoma is found to be present all around the world. It is projected that the individuals who are suffering from glaucoma majorly belong to the age group of 40- 80 years of age and will be accounting for 3.4% by the year 2040. As a result, 112 million will be suffering from glaucoma all around the world. Hence, the improvements that are brought in the field of analysis and diagnosis of glaucoma will help all the medical healthcare practitioners, clinicians and patients to detect and evaluate the structural changes and damages made by the disease effectively. It also includes identifying the treatment possibilities, adoption of preventive visual disabilities and correct prognosis that go on for a long term basis.

Jonas, Gusek, Guggenmoos-Holzmann, & Naumann, (1988) identified that glaucoma is the disease that majorly belongs to the area of the optic nerve system and classifies the excavation and corrosion of the neuroretinal rim. It also marks the cupping of the optic nerve head (ONH) that has increased inaccessibly. However, due to the virtual and five-fold formation in the ONH area, the virtual presence of disc ratio (CDR) is found to be missing in the cup and mainly highlighted the presence of other detections related to pathological cupping. Ting, Cheung, Lim, Tan, Quang, Gan & Wong, (2017) examined that the advanced computed algorithms that have been trained and assembled to identify the presence of glaucoma-like disc are also used by the medical practitioners to define the vertical CDR level that is valued at 0.7 and 0.8 levels correspondingly. Christopher, Belghith, Weinreb, Bowd, Goldbaum, Saunders & Zangwill, (2018) examined that the investigators who are working the ophthalmology field also use the high definition machine learning tools to create differences between the glaucomatous nerve fiber layers that are known to damage the normal functioning of the OCTs. It was also analyzed by the ophthalmologists that the OCT essentially works in the OCTs (9x 12 mm) levels and is necessary to be recognized so that the damages caused by them are reduced efficiently.

Li, He, Keel, Meng, Chang & He, (2018) examined that the ophthalmologists also investigated about the future opportunities that are related to training of neural networks and recognizing of the visual field (VF) loss that is present all across the spectrum of disc size. Hence, it can be said that the DL could be used as an effective means to identify the slow changes that are occurring in the structural optic nerve of glaucoma. Elze, Pasquale, Shen, Chen, Wiggs & Bex, (2015) analyzed that ophthalmologists also investigate the presence of retinal ganglion cell axons atrophy in the confines of ONH. It belongs to the manifestation of the low dimensional psychophysical data so that the detection of the VF progression exit is executed accurately.

The evaluation of the different facts related to the worldwide incidences of the point-wise assessment and sectoral VF assessment are also included in the study. However, it must be noted that the examination and alignment of the approaches are not allied and there is the development of clinical ground truth which is not correlated to each other. As a result, there is a loss of VF from glaucoma and the method proves to be useful for the assessment and early-stage identification of VF loss.

According to Yousefi, Kiwaki, Zheng, Sugiura, Asaoka, Murata & Yamanishi, (2018) there is also the development of the machine-based algorithm that is responsible for the detection of the VF progression in the earlier stages. It is also regarded as a better method than the conventional strategies so that the quantitative facts related to the regional VF progression are known precisely and provides information related to the expected changes in the future. Kass Heuer, Higginbotham, Johnson, Keltner, Miller & Gordon, (2002) examined that intraocular pressure (IOP) is also found on the glaucoma progression when the process of progression is delayed. As a result, there is a lowering of the IOP and the diseased stages of the patients are identified effectively.

Hence, it can be said that the optimized treatment regimens have not been identified regarding the different stages of glaucoma and vital studies are necessary to be carried in this segment to identify more regiment curative methods.

According to Kazemian, Lavieri, Van Oyen, Andrews & Stein, (2018), the development of the clinical forecasting tool has been done to analyze the tonometric and VF data that are related to the projection of disease trajectories that occur at the different levels of targeted IOPs. (58) Examined that the investigations by ophthalmologists also suggest the amalgamation of the other ophthalmic and non-ophthalmic data so that best strategies could be adopted to establish the concise with the targeted IOPs. Additionally, the incorporation of machine learning is done to execute the analysis related to the diagnosis of glaucoma harbor fears and the data related to genome-wide aspects. It was also examined that the genome-wide data majorly includes lifestyle attitude of the patients so that the examination of the glaucoma harbor fears are executed effectively. The medical history and forecasting algorithm methods are also used to conduct the early prognostication process so that future risks related to invasive surgery or loss of eyesight vision because of glaucoma are identified precisely. The machine learning algorithm process also helps in revising and enhancing the practice of ophthalmologists so that the diagnosis of glaucoma and other disease progression rates are identified optimally. Hence, it can be said that the adoption of new technologies like the machine learning algorithm helps in clinical forecasting, overall prognosis and treatment without delay successfully.

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Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data (GRDJE/ Volume 4 / Issue 11 / 002)

E. Retinopathy of Prematurity According to Blencowe, Moxon & Gilbert, (2016), Retinopathy of prematurity (ROP) is regarded as one of the major causes of blindness among children globally. The annual incidence of the disease has been recorded to be 32000 globally while the regional incidence of the disease varies as per the preterm births, oxygen exposure, neonatal mortality, and other factors. Fielder, Shaw Robinson & Ng, (1992) examined that ROP screening is executed among children with the help of ophthalmoscopic inspection or telemedical valuation in which the use of digital fundus photography is applied. All these processes help in the identification of the early signs of the ROP so that treatment could be started at the early stage and most cases of ROP blindness will be avoided.

Campbell, Swan, Jonas, Ostmo, Ventura, Martinez-Castellanos & Chan, (2015) examined that in the screening of ROP, subjective diagnosis of ROP and lack of trained examiners highly impact the proper conduction of the ROP screening worldwide.

According to Daniel, Quinn, Hildebrand, Ells, Hubbard, Capone & Ying, (2015) telemedicine has, therefore, emerged as an essential model to make assessments of the ROP and is used by at least 25% of the telemedicine examiners to provide accurate data to the physicians. Worrall, Wilson & Brostow, (2016) analyzed that efforts are being laid down to incorporate DL into the automated diagnosis of ROP so that the obstacles in the screening process of ROP will reduce. (19) Determined that the use of DL will help in the diagnosis of plus disease and serve ROP with an AUC ranging up to 9.8. As a result, it will show better results in comparison to the image-based diagnosis and ophthalmoscopy processes. Brown, Campbell, Beers, Chang, Donohue, Ostmo & Chiang, (2018) ascertained that in comparison to the worldwide ROP diagnosis, the i-ROP DL process is found to be more effective and recommended by most of the ROP experts. Hence, it can be said that the i-ROP DL system is capable to effectively monitor, check progression, regression and treatment response.

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Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data (GRDJE/ Volume 4 / Issue 11 / 002)

F. Data Modeling According to Hwang, Alexander, Casper, Starren, Cimino & Chiang, (2006) Data modeling is the process that is used to resolve the ambiguity that is present in the medical language at the time of presenting the same conceptions and terminologies so that effective communication is established with different healthcare centers and information systems. The technologies are found to highly useful in mapping the medical term that are having common concepts by grouping the similar objects and making provisions for different clinical analysis, retrospective data investigation, clinical trials, and proof-based practices. ICD-10 which is based on the structured hierarchy is effectively used for the diagnosis of disease. On the other hand, AI is used by the physician to make a decision based on the arranged hierarchical structure. The ICD-10 coding also helps in the coding of eye disease and uses multiple levels to identify the existing eye problem. For example, the ICSD-10 coding technique is used to identify the symptoms by using a slit assessment. The doctors' major issues while recording the data at the time of conducting diagnosis because the doctor could not leave the patient unattended at the time of screening to record the data. Hence, during the inspection of the disease, the recording of the vital patient specifications becomes nearly impossible. Therefore, data modeling is used as an efficient method to make entry of the data with the help of web applications so that there will accurate data entry by medical experts by a click of the mouse. In this case, the healthcare exert needs to point at the plus symbol of Cornea and select the next option by clicking “Endothelium� in the dropdown menu to reach the next level of options. As a result, with the help of the three-step click method, the other options will appear along with the definition of the symptoms. At last, the last option is chosen and all the levels selected will be concatenated and produce accurate results. The use of graphical interference could also be executed to present the diagnosis format along with data recording. Hence, it can be said that machine-learning algorithms have become the most effective way of matching and presenting the data accurately.

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Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data (GRDJE/ Volume 4 / Issue 11 / 002)

G. Potential Challenges According to Gulshan, Peng, Coram, Stumpe, Wu, Narayanaswamy & Kim,(2016) in the identification and screening of eye diseases are still a problem because there are clinical and technical challenges in the usage of AI-based models in disease screening in ophthalmology. Ting, Cheung, Lim, Tan, Quang Gan & Wong, (2017) examined that issues like AI training, retinal image testing difficulties, image magnification issues, and others are faced by the clinicians. Moreover, there are issues related to the identification of large amounts of data set availability for the common and rare diseases which creates difficulty in eye care practices. The diseases like ROP and glaucoma are highly interobserver and have differences in disease phenotype which are at the time not identified precisely. Krause, Gulshan, Rahimy, Karth, Widner, Corrado & Webster,(2018) analyzed that the adjudication grades by retina require a more accurate and rigorous reference standard for the detection of missed microaneurysms in DR. However these features are not provided by the existing model of AI and more improvements are required in the AI algorithm performance.

Ting, Pasquale, Peng, Campbell, Lee, Raman & Wong, (2019) examined that even though there is large scale acceptance of AL in the healthcare practices, the experts, clinicians, and patients still face the issues related to the usage of AI and DL and consider them to be ‘black-boxes’. Hence, in the lack of physician acceptance, the use of quantitative algorithmic performance is not widely accepted in the screening of eye diseases. Moreover, there are issues related to generating heat maps and interpretation as they cover the area of vitreous of an OCT patient which is difficult to analyze and code. Additionally, there was an absence in the presentation of reports that were related to the diagnostic of the performances of the DL systems. The reports also did not mention the power calculation and independent data set that are necessary to investigate the severity of the disease. As a result, due to the absence of power calculation, the details related to the prevalence of the disease, identification of type1 and 2 errors, information related to desired precision were not recorded.

According to Keel, Lee, Scheetz, Li, Kotowicz, MacIsaac & He (2018) the current AI screening systems that are used for DR are generally based on the two-dimensional images and do not have many stereoscopic qualities. As a result, the recognition of the prominent lesions such as retinal tractions becomes difficult. There are issues related to patient acceptance with the involvement of an AI-based DR screening system as many patients do not entrust machines for clinical care. All these issues are faced because of different populations, settings, and patient’s acceptability which could be only be improved by incorporating AI algorithms for conducting multimodal imaging. It will help to receive records and report more accurate information related to eye disease screening effectively.

Hence, as per the above literature review and articles methane, the research work is related to medical and related domains that are highly focused on using sophisticated methods to improve the classification accuracy. In addition to this, the research also includes the general framework for the classification of eye disease and based on symptoms that are recorded in the form of standard taxonomies directly by medical experts. Furthermore, the study also involves a two-step diagnostic process which includes patient data related to the refractive error that was filtered out before analysis. In the future, the study also includes image-based test results that will be directly translated into the system of hierarchy for predication of disease in a better manner. However, with the help of computer technology, it is known as a technique and expert method to resolve the issues in the near future. Hence, it is essential for the user interface to develop data recording with is unique and has been admired for its ease of use.

V. SIGNIFICANT OF STUDY DL is based on AI machine learning and has received clinical acceptance for the screening of retinal diseases like DR and ROP all around the world. However, future research is essential to be conducted in this field so that the deployment of more cost-effective techniques could be made in the clinical assessment of varied DL system and introduce cost-effective means of DL clinical practices. Moreover, it is also essential to unravel the black-box nature of prevailing DL systems so that future methodologies will be identified. It will also help in the reduction of the challenges and increased the practice of DL in diseases in ophthalmology in the future. Moreover, it will also help in laying down the foundation of the categorization of the eye disease, record and report standard taxonomies to the clinical experts. Additionally, there will be incorporation of anterior and posterior segment symptoms and general health information so that a correlation will be established between the disease and the symptoms. The use of machine learning algorithms will also help in the two-step screening procedure through which the refractive error and could be filtered accurately. As a result, the use of image-based evaluation will help in the direct translation of the symptom hierarchy and better identification and prediction of the disease.

VI. RESEARCH METHODOLOGY It is known as a process by which the data will be collected by using different types of tools and instruments. It enables the researcher to examine different types of sets and find out the best way of collecting and analyzing the different facts and figures in an ethical manner. To collect the data it includes different research types of philosophies, practices, and approaches by which the researcher will be able to make effective decisions by choosing the best variable. It also includes the research approach and strategy that are classified into qualitative ad quantitative. The research approach is also a part of the study which is based on the positivism and interpretivism research approach and includes different investigations and theories in an effective manner. At last, data analysis is another part of the research methodology by which the researcher will be able to analyze the collected data in the form of

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Investigate a Diagnosis of Eye Diseases using Imaging Ophthalmic Data (GRDJE/ Volume 4 / Issue 11 / 002)

thematic analysis and statistical analysis. Hence, it t can be said that research methodology is a combination of several methods, approaches, and principles as per the desired manner.

In the current study to investigate the diagnosis of eye disease by using imaging orthographic data, the researcher mainly focuses on the quantitative research approach by collecting all these data that are momentarily expressed. Additionally, the study also includes experimental descriptive research such as design for collecting different variables related to eye disease and orthographic data in order to diagnose eye disease. Data analysis is to be used for analyzing the result based on the collected data with the help of statistical analysis and thematically analysis. In the current study, the thematically analysis is to be used which includes plotting the value in a tabular manner and presenting the result in a graph, bar chart, pie chart. In addition to this, to get more accurate results and to test the hypothesis and reliability with the help of factor analysis, there are many statistical tools SPSS, and ANOVA, Chi-square test will be used.

A. Methodology Adopted All the existing studies are based on the disease so that the algorithm developed and used to test the specific eye disease such as glaucoma disease predication indifference (13). Although no general solution is available because of the lack of standardization in recording medical information and difficulty that are included in the general automated solutions. Hence, it is known as critical challenges while applying AI and Machine Learning method to obtain the data by multiple experts for diagnosis and description of the disease. Hence, it can be said that there is a requirement of the standardized framework to provide direct data entry by experts and mainly include efficient and possibility in treatment that may facilitate classification.

B. Data Collection and Description In the present study, the dataset is composed and based on real-time data that define AL data mining and algorithm. In order to do this, professional doctors directly recorded the data for research and development. To collect the data it includes, the size and description of the data set that are classified on the basis of 10 attributes such age gender, compliant, Va OD (Right eye), Va OD (left eye), pinhole (left eye) pinhold (right eye) , slit-lamp exam, posterior segment exam, and diagnosis class.

C. Method There are several classifications of algorithms that will be used in the medical domain which provides easily reliability and confidence. It will be classified as per data whether parametric and non-parametric as well as tree-based method Random Forest are non-parametric classifiers will be applied to data whose distribution is not known. In order to collect the medical domain with the help of normally distributed data is almost impossible so that non- parametric method is to be used. The study also includes other classifiers such as neural network, support vector machine that is known as a popular way in classifying the diagnostic data but it is only used based on the assumption to simplify the learning process so that sometimes, it creates higher error rate.

Additionally, some other classes of disease such as cancer, support vector machines play an important role in this kind of disease.

Hence in the current research, the researcher used mixed and both types of classification of algorithms such as decision tree, random forest, and neural method so that the researcher will be able to identify the impact of propose data modeling method by classifying the accuracy. In the current study, the dataset is to be used is dived into two sets such as training and testing with 70:30 split. In order to conduct validation 10-fold cross-validation method is to be used.

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