A Modern Approach to Overcome The Stress Levels of Patients By Revealing Success Rate of the Special

GRD Journals- Global Research and Development Journal for Engineering | Volume 2 | Issue 2 | January 2017 ISSN: 2455-5703

A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist S. Laxmi Assistant Professor Department of Information Technology St. Martin’s Engineering College, Hyderabad Tata A S K Ishwarya Assistant Professor Department of Information Technology St. Martin’s Engineering College, Hyderabad

S. Sreeja Assistant Professor Department of Computer Science & Engineering St. Martin’s Engineering College, Hyderabad

Abstract In the current modern era people are prone to many diseases yet some are curable some are not. Diseases which are identified earlier stage can be rectified easily it is applicable same for both living and non-living things. For an instance non-living things be it a machine when a problem encountered in initial stage solved immediately will reduce the cost factor. Similarly if a disease is identified in early stage it would be curable and can be solved easily, where as in the final stage risk is more success factor cannot be predicted. When the person is in the final stage of the disease patient will go through the multiple feelings that is stress level will be increase because of which patient may not be positively respond to the treatment. If the problem can be solved in few months of span it would be increased to more months because of the stress level. Considering the same aspect to the machine, when a machine encounters the problem, the problem will be rectified by human being and machine will not bothered about the problem because it doesn’t get carried away by emotions. Keywords- Data Mining, curability, stress level, risk, initial stage, predicted

I. INTRODUCTION In the current medical scenario if a person encounter with disease the specialist will let the patient know the treatment procedure and if in case they are in the advanced stage they will explain about severity of the issue and they will not assure about complete success, because success or failure not in the hands of doctor but the way the patient responds to the treatment. The resultant factor is that the person is going through the stress level since they are not getting complete assurance from doctor; this stress level is in turn killing the person from inside. Health information seeking online is generally done by health consumers and these health consumers probably classified into patients relatives, patients friends and citizens in general [3]. Searching factors varies based on the search experience levels [2]. Health consumers who are educated generally prefer online healthcare through high speed internet that is available at home and work [11, 12]. Presently a research done on the search factor states that females will seek more information on internet than males [1, 4, 6, 8]. The world health organization indicates that 29% of the health consumers decide through internet whether they have to attend the particular doctor or not and to take appointment from the doctor or not [5]. In the latest pew internet project states that 59% of the newly diagnosed patients seek the second opinion and ask the questions to the doctor by means of internet [9]. Health information seekers generally feel reassure after having consulted health information sites [6, 5, 7, 10].

II. LITERATURE SURVEY A. Data Mining Applications In Healthcare Sector This paper discusses about data mining tools that are used to gain the relevant information in health care sector by removing irrelevant information, in this a comparative study has been done by using different methodologies and algorithms [3]. B. Better Healthcare with Data Mining In this paper they discussed about clinical practice and evidence based care using PASW modular and complex inter relational ships using decision making rules from complex data [7].

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

C. Data Mining Applications in Healthcare In this paper they discussed about how healthcare organization has to be maintain and how customer relationship has to be maintain for the growth of the organization, and this article mainly focuses on health care applications using data mining [12]. D. A review of data mining using big data in health informatics In this paper they discussed about big data that Big data grants limitless tools and possibilities for healthcare sectors and promising aspects to gain the most knowledge in healthcare sector [7]. E. Proposed methodology To solve this problem we are providing with the success rate of the patients who have gone through the same disease and are in the final stage, by revealing that data to the patient it boosts up the patient confidence and it gives them confidence of being alive and faith that they can have a healthy life like others.

III. IMPLEMENTATION In this paper we have focused on doctor efficiency based on their curability rate. Here we have considered a sample data where we have considered two doctors and their efficiency rate. In this sample data five patients have visited these two doctors in 3:2 ratio out of which one doctor has produced 100% result. So the patient ultimately opts for second doctor who produced 100% result. The source of credibility and trust in websites has become very important because of considering the following factor. 1) The site has to be easy to use for novice users. 2) Generally people that are health consumers believe in the advice that comes from a knowledgeable source. 3) The advice appeared here are prepared by an expert that is impartial and independent. 4) The reasoning behind the advice is explained here. The advices here are presented in a website that is impartial, independent and genuine based on the expertâ&#x20AC;&#x2122;s survey. Here we are considering a sample data belonging to different doctors of different stream. And we are considering the success factor of the doctor; in this we are generating the decision tree. Simple search times and large amount of fact sources makes the health consumer to be in the convenient end. In this health profession should respond to a question without any defensive nature by asserting their expert of opinion and making sure that the patient wonâ&#x20AC;&#x2122;t feel threatened. The reliable and accurate information has to be available on website to the patient. The relationship of trust and cooperation should be maintained between physical and his/her patient. In this paper we are trying to present facts that would be helpful gaining the confidence levels. Age specific lifestyle is influenced and typical health status as the decease risk changes as a result of aging. The results are based on the websites run by organization. Home page of individual doctors and online support groups where people exchange health information and blogs authorized by health advocates or those pursuing self-help, when limited time is available health profession have to undertake search with the lack of coherent available information effectively. It is important to increasing the differentiate difficult Dependable and precise up to date health resources for health profession should be available. Seriously concerned about the quality trust worthiness should not be compromised. The implementation is truly based on targeting professional and citizen trust and credibility based on health related information access of the internet including those pictures that is those doctors who are hard to reach. In this evidence suggest that to recommend those physician and replacement for health professions and direct guidance would be provided doctors need to be able to refresh their memories and updated knowledge effectively and efficiently, when patient ratio gets declined. When a person goes through this website automatically they will have a confidence about their curable ratio.

IV. RESULTS Dr. Disha curable ratio is 3 so success ratios is high

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

Fig. 1: Preprocessing data for Cardiology

Fig. 2: Classification data for Cardiology

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

In this Fig 1 is preprocessed for cardiology based on doctorâ&#x20AC;&#x2122;s name and their count that is where count varies on number of cases handled and the success rate. In Fig 2 data is classified for cardiology and finally classifier model that contains set of rules is obtained Decision table contains total number subsets and search direction is calculated. Dr. Roma Success rate is 3

Fig. 3: Classification data for Dentistry

Fig. 4: Preprocessing data for Dentistry

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

In this Fig 3 data is classified for Dentistry and finally classifier model that contains set of rules is obtained Decision table contains total number subsets and search direction is calculated. In Fig-4 data is preprocessed for Dentistry based on doctorâ&#x20AC;&#x2122;s name and their count that is where count varies on number of cases handled and the success rate. Dr. Adwik 3 success rate curable

Fig. 5: Preprocessing data for ENT

Fig. 6: Classification data for ENT

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

In this Fig5 data is preprocessed for ENT based on doctorâ&#x20AC;&#x2122;s name and their count that is where count varies on number of cases handled and the success rate. In Fig 6 data is classified for ENT and finally classifier model that contains set of rules is obtained Decision table contains total number subsets and search direction is calculated. Dr. Adwik 3 success rate curable

Fig. 7: Preprocessing data for Gynecology

Fig. 8: Classification data for Gynecology

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

In Fig 7 data is preprocessed for Gynecology based on doctorâ&#x20AC;&#x2122;s name and their count that is where count varies on number of cases handled and the success rate. In Fig 8 data is classified for Gynecology and finally classifier model that contains set of rules is obtained Decision table contains total number subsets and search direction is calculated. Dr.mohi Success ratio is high 2

Fig. 9: Preprocessing data for Oncology

Fig. 10: Classification data for Oncology

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

In Fig 9 data is preprocessed for Oncology based on doctors name and their count that is where count varies on number of cases handled and the success rate .In Fig 10 data is classified for Oncology and finally classifier model that contains set of rules is obtained Decision table contains total number subsets and search direction is calculated. Based on the degree Deg(n)=(deg+(n)+(deg-(n)) Relative absolute vulnerability values based on individual relative and absolute value a metric is developed in online social network for patient and doctor ratio. VA =VI *VR VI ={Vi1,Vi2,---------,Vin} VR={Vr1,Vr2,------,Vrn} In an instance where patients have attended the doctors where lowest cure cases is equivalent to VA=MIN(VI,VR) In an instance where patients have attended the doctors where highest cure cases is equivalent to VA=MAX(VI,VR)

90 80 70 60 50 40 30 20 10 0

cure ratio Success rate

patient 1

patient 3 Graph 1.1

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Column1 patients approached

DOC 1 DOC 2 DOC 3 DOC 4 Graph 1.2

The results stated above are based on the success and cure ratio. The graph 1.1,1.2 above explains above depicts the patients approached to a doctor and their success ratio

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

S.No 1

2 3 4 5

Table 1: Doctor Name a)Dr.Disha Cardiologist b)Dr.Nisha c)Dr.Raju a)Dr.Roma Dentist b)Dr.Aditya c)Dr.Adwik a)Dr.Adwik ENT b)Dr.Aditya a)Dr.Disha Gynecologist b)Dr.Nisha a)Dr.Mohi Oncologist b)Dr.Nisha Specialist

Success Rate 80% 60% 53.30% 93.30% 66.60% 53.30% 93.30% 66.60% 60% 53.30% 93.30% 80%

V. SUMMARY SHEET By considering doctors who are good in a particular sphere we are trying to justify the fact that when a patient attends a doctor they have to do a proper research that is how many cases are success or failure Following are the sphere considered Cardiologist, Dentist, ENT, Gynecologist, and Oncologist. The doctors dealing with those spheres are respectively as follows Dr.Disha, Dr.Nisha, Dr.Raju, Dr.Roma, Dr.Aditya,Dr.Adwik,Dr.Adwik, Dr.Aditya, Dr.Disha, Dr.Nisha, Dr.Mohi, Dr.Nisha. Calculative methodology followed in this is based on Precision and Recall, the success ratio of the cases handled is as follows 80%, 60%, 53.3%, 93.3%, 66.6%, 53.3%, 93.3%, 66.6%, 60%, 53.3%, 93.3%, 80%. Calculative formulas used Factual class A (TA) - Properly classified into class A Deception class A (FA) - Erroneously classified into class A Factual class B (TB) - Properly classified into class B Deception class B (FB) - Erroneously classified into class B Precision = TA / (TA + FA) Recall = TA / (TA + FB) You might also necessitate accurateness and F-measure: Accuracy = (TA + TB) / (TA + TB + FA + FB) F-measure = 2 * ((precision * recall)/(precision + recall)) True class is successful operations with respective to the above context, Failure class is unsuccessful operations with respective to the above context. In this successful operations or failure operations is calculated as

For instance total number of operations is 15 and successful is 12 then success ratio is 12/15Ă&#x2014;100=80%. To calculate the failure percentage subtract success percentage from 100% 100%-80%=20% failure percentage is 20% Table 2: S.No 1

2 3 4 5

Doctor Name/ pecialization a)Dr.Disha/ Cardiologist b)Dr.Nisha/ Cardiologist c)Dr.Raju/ Cardiologist a)Dr.Roma/ Dentist b)Dr.Aditya /Dentist c)Dr.Adwik/ Dentist a)Dr.Adwik/ ENT b)Dr.Aditya/ ENT a)Dr.Disha/ Gynecologist b)Dr.Nisha/ Gynecologist a)Dr.Mohi/ Oncologist b)Dr.Nisha/ Oncologist

Result Dr.Disha = Successful doctor Dr.Nisha = Failure doctor Dr.Raju = Failure doctor Dr.Roma = Successful doctor Dr.Aditya = Failure doctor Dr.Adwik = Failure doctor Dr.Adwik = Successful doctor Dr.Disha = Successful doctor Dr.Mohi = Successful doctor

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A Modern Approach to Overcome the Stress Levels of Patients by Revealing Success Rate of the Specialist (GRDJE/ Volume 2 / Issue 2 / 004)

VI. CONCLUSION In this paper we have given a brief idea to the patients as well the hospitals that is when a patient in a dilemma state to choose one doctor over the other then hospitals have to take a lead step. And should reveal them about the mined data of the doctor success rate then ultimately the patient will consult the doctor and they will have a hope that they are going to get over the disease lead a happy and blissful life.

REFERENCES [1]

Atkinson NL, Saperstein SL, Pleis J. Using the internet for health-related activities: findings from national probability sample. Journal of Medical internet Research 2009;11(1):e4. [2] Lorence DP, Park H, Fox S. Assessing health consumerism on the web: a demographic profile of information- seeking behaviours. Journal of Medical Systems 2006;30(4):251-258. [3] Sorensen T. Health Trends WHO/eHealth consumer trends survey 2005, presentation at joint meeting of the SANCO Health systems working party and the INFSO eHealth Working group, Luxemburg [cited 1 April 2010].Available from: http://ec.europa.eu/health/ph_information/implement/wp/systems/docs/ev_20051201_co19_en.pdf [4] Fox S, Jones S. The social life of health information, Pew internet & American Life Project 2009 [cited 29 March 2010]. Available from: http://www.pewinternet.org/Reports/2009/8-The-Social-Life-of-Health-Information.aspx. [5] Sorensen T. eHealth trends: WHO/European eHealth consumer trends survey final report. Luxembourg: Health & Consumer Protection Directorate General; 2008. [6] Andreassen HK, Bujnowska-Fedak MM, Chronaki CE, Dumitru RC, Pudule I, Santana S, et al. European citizens use of e-health services: a study of seven countries. BMC Public Health 2007;7(53). [7] Fox S. Online Health Search 2006, Pew internet & American Life Project [cited 29 March 2010]. Available from: http://www.pewinternet.org/Reports/2006/Online-Health-Search-2006.aspx. [8] Gallagher S, Tedstone Doherty D, Moran R, Kartalova-Oâ&#x20AC;&#x2122;Doherty Y. internet use and seeking health information online in Ireland: demographic characteristics and mental health characteristics of users and non-users. HRB Research Series 4. Dublin: Health Research Board; 2008. [9] Fox S. The engaged E-patient population, Pew internet & American Life Project 2008 [cited 29 March 2010]. Available from: http://www.pewinternet.org/Reports/2008/The-Engaged-Epatient-Population.aspx. [10] Ybarra M, Suman M. Reasons, assessments and actions taken: sex and age differences in uses of internet health information. Health Education Research 2008;23(3):512-521. [11] Wangberg SC, Andreassen HK, Prokosch H-U, Santana SMV, Sorensen T, Chronaki CE. Relations between internet use, socio-economic status (SES), social support and subjective health. Health Promotion International 2008; 23(1):70-77. [12] Kummervold PE, Chronaki CE, Lausen B, Prokosch H-U, Rasmussen J, Santana S, Staniszewski A, Wangberg SC. eHealth trends in Europe 2005-2007: a population-based survey. Journal of Medical internet Research 2008;10(4):e42

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A Modern Approach to Overcome The Stress Levels of Patients By Revealing Success Rate of the Special

Published on Feb 6, 2017

GRD Journals

In the current modern era people are prone to many diseases yet some are curable some are not. Diseases which are identified earlier stage can be rectified easily it is applicable same for both living and non-living things. For an instance non-living things be it a machine when a problem encountered in initial stage solved immediately will reduce the cost factor. Similarly if a disease is identified in early stage it would be curable and can be solved easily, where as in the final stage risk is more success factor cannot be predicted. When the person is in the final stage of the disease patient will go through the multiple feelings that is stress level will be increase because of which patient may not be positively respond to the treatment. If the problem can be solved in few months of span it would be increased to more months because of the stress level. Considering the same aspect to the machine, when a machine encounters the problem, the problem will be rectified by human being...