Medical Assistant Design during this Pandemic Like Covid-19

International Journal of Electrical, Electronics and Computers Vol-6, Issue-3 | May-Jun, 2021 Available: https://aipublications.com/ijeec/ Peer-Reviewed Journal

Medical Assistant Design during this Pandemic Like Covid-19 Kshitij Chouhan, Mukul Porwal, Rahul Gupta, Rahul Mehta, Loveleen Kumar 1,2,3,4B.Tech 5Assistant

Students, Department of Computer Science and Engineering, Global Institute of Technology, Jaipur, Rajasthan, India.

Professor, Department of Computer Science and Engineering, Global Institute of Technology, Jaipur, Rajasthan, India.

Received: 02 Jun 2021; Accepted: 18 Jun 2021; Date of Publication: 23 Jun 2021 ©2021 The Author(s). Published by Infogain Publication. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Abstract— In the current world scenario, individuals square measure additional involved regarding their health.

However, it's terribly troublesome to get consultation with the doctor just in case of any health problems. Since the invention of the Coronavirus (nCOV-19), it's become a world pandemic. At an equivalent time, it's been a good challenge to hospitals or health care employees to manage the flow of the high variety of cases. particularly in remote areas, it's becoming tougher to consult a doctor once the immediate hit of the epidemic has occurred.

So, to steer an honest life, care is incredibly vital. The planned plan is to form a medical chatbot victimization Machine Learning algorithm which will diagnose the illness and supply basic details regarding the illness before consulting a doctor. Several studies will solve this downside with some reasonably chatbot or health assistant.

This project report proposes a colloquial care larva that's designed to order, counsel and provides data on generic medicines for diseases to the patients. During this paper, we would like to explore and deepen additional information regarding chatbots that would facilitate individuals to urge an equivalent and correct treatment as a doctor would do. In addition, presenting a virtual assistant may live with the infection severity and connect with registered doctors once symptoms become serious.

Keywords— Chatbot Health service, Prediction, Pattern matching, Disease, Query processing.

I.

INTRODUCTION

We have seen robot’s activity and capital punishment jobs while not human help in over the last ten years. Not simply the mechanical robots however additionally machinecontrolled programs which might modify their output supported self-learning. Use of machine learning is increasing hugely within the industry. There are multiple industries that have considerably wedged through machine learning. The tending sector isn't completely different.

Without delay Machine learning or AI is getting used in diagnosing and treatment. Use of Pattern matching has accumulated in sickness detection supported health knowledge. the combination of Machine Learning in tending with a Chabot as your doctor is about to witness a major paradigm shift.

After the increase of the net and mobile apps, virtual chatbot applications are the newest inventions of digital style. These applications are unit documented for automatic informal agents that run on programming or a sort of computer science (AI) interaction between the users ISSN: 2456-2319 https://dx.doi.org/10.22161/eec.63.5

and machines with the intervention of language process (NLP). Chatbots are probably spoken because of the most promising and advanced style of human-machine interactions. Eventually, these virtual agents are becoming concerned within the main international sectors like health care, banking, education, agriculture, etc. The health care sector is closely related to human interaction, and it appears unreasonable that informal AI applications like chatbots are a lot of rife. Hospital director’s area unit payment their day in appointment planning and respondent routine queries of patients. continued or repetition of consonant actions and words is neither necessary nor productive. Such jobs may be simply finished larva applications. It's obvious that patient feedback assessments are doable by grouping user responses to keep up smart patient flow. Within the prevalence of great pandemics like novel Coronavirus (nCOV-19), health bots are a unit useful as a supplement to private clinical care or immediate medication Through chat bots one will communicate with text or voice interface and find reply through computer science. Typically, a talk larva can

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Medical Assistant design during this Pandemic Like COVID-19

communicate with a true person. Chat bots are a unit utilized in applications like ecommerce client service, decision centers and web diversion. Chatbot’s area unit programs designed to mechanically have interaction with received messages.

awfully active space of analysis and development, there's not one agreed-upon definition that will satisfy everybody, however there are some aspects, which might be a part of any knowledgeable person’s definition. The definition principally offers is:

Chat bots are often programmed to reply in a constant manner on every occasion, to reply otherwise to messages containing sure keywords and even to use machine learning to adapt their responses to suit matters. A developing range of hospitals, nursing homes, and even personal centers, presently utilize on-line Chat bots for human services on their sites. These bots connect with potential patients visiting the positioning, serving to discover specialists, booking their appointments, and obtaining them access to the proper treatment. A milliliter model must be created whereby we have a tendency to might provide any text input and on the idea of coaching information it should analyze the symptoms. A supervised supply Regression machine learning formula is often enforced to coach the model with information sets containing numerous disease CSV files. The goal is to match outputs of assorted models and recommend the simplest model which will be used for symptoms in realworld inputs. The information set contains a CSV file having all diseases compiled along. The supply regression formula in milliliter permits the United States to method the information with efficiency. The goal here is to model the underlying structure or distribution of the information so as to find out a lot from the coaching set. In any case, the employment of AI in AN trade wherever individuals’ lives may be in question, still starts misgivings in people.

It brings up problems regarding whether or not the tasks mentioned above need to be allotted to human employees.

This attention chatbot system can facilitate hospitals to produce attention support on-line 24x7, it answers deep moreover as general queries. It additionally helps to come up with leads and mechanically delivers the data that ends up in sales. By asking the queries serial it helps patients by guiding what specifically he/she is trying to find.

DEFINITION: Ability of a machine to boost its own performance through the utilization of a code that employs AI techniques to mimic the ways in {which} by which humans appear. Machine Learning (ML) could be a subfield of AI (AI) that considerations developing process theories of learning and building learning machines. The goal of machine learning, closely let alone the goal of AI, is to attain an intensive understanding concerning the character of the training method (both human learning and alternative varieties of learning), concerning the process aspects of learning behaviors, and to implant the training capability in laptop systems. Machine learning has been recognized as central to the success of AI, and its applications in numerous areas of science, engineering and society.

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PURPOSE & SCOPE

The purpose of this project is to collect the patients’ medical history of records and filter it appropriately by applying data preprocessing techniques. Once the data comes into the structured shape it can then be fed into the relational database structure of MS Excel file. The admin also needs to monitor the predictions and replies of the model to ascertain quality. ●

TECHNOLOGY USED

Machine Learning (ML) is the computerized approach to analyzing machine work that supports a collection of theories and a collection of technologies. And, being an ISSN: 2456-2319 https://dx.doi.org/10.22161/eec.63.5

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ALGORITHMS USED

DECISION TREE CLASSIFIER: Decision tree is the most powerful and widespread tool for classification and prediction. A choice tree could be a flow chart like tree structure, wherever every internal node denotes a check on Associate in Nursing attribute, every branch represents Associate in Nursing outcome of the check, and every leaf node (terminal node) holds a category label.

A tree may be “learned” by cacophonous the supply set into subsets supported AN attribute worth taking a look at.

This method is continual on every derived set in a very algorithmic manner known as algorithmic partitioning.

The rule is completed once the set at a node all has constant worth of the target variable, or once cacophonous not adds worth to the predictions. the development of a choice tree classifier doesn't need any domain information or parameter setting, and so is acceptable for explorative information discovery. call trees will handle high dimensional information. In general, the choice tree classifier has smart accuracy. call tree induction could be a typical inductive approach to be told information on classification.

STRENGTHS AND WEAKNESS OF DECISION TREE APPROACH The strengths of decision tree methods are: • Decision trees square measure ready to generate comprehendible rules.

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• Decision trees perform classification while not requiring a lot of computation.

• Decision trees square measure ready to handle each continuous and categorical variable.

• Decision trees offer a transparent indication of that field square measure most significant for prediction or classification.

The weaknesses of decision tree methods: • Decision trees are a unit less applicable for estimation tasks wherever the goal is to predict the worth of neverending attributes.

• Decision trees are a unit vulnerable to errors in classification issues with several categories and a comparatively tiny range of coaching examples.

• Decision trees will be computationally overpriced to coach. The method of growing a call tree is computationally overpriced. At every node, every candidate cacophonous field should be sorted.

Medical Assistant design during this Pandemic Like COVID-19

II.

METHODOLOGY

The Health-Care Chatbot System ought to be written in Python, GUI links and a straightforward, accessible network API. The system should offer a capability for simultaneous operation and system style mustn't introduce measurability problems with respect to the amount of surface computers, tablets or displays connected at any time. the top system ought to additionally provide seamless recovery, while not information loss, from individual device failure. There should be a robust audit chain with all system actions logged. whereas interfaces are price noting that this method is probably going to evolve to what's out there. Thereupon in mind, the foremost pliable and transportable technologies ought to be used for the implementation. The system has criticality to this point because it could be a live system. If the system is down, then customers should not notice, or notice that the system recovers quickly (seconds). The system should be reliable enough to run, crash and bug free a lot of or less indefinitely, or facilitate error recovery sturdy enough such glitches are ne'er discovered to its endusers.

PROJECT DESIGN ScreenShots:

ISSN: 2456-2319 https://dx.doi.org/10.22161/eec.63.5

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III.

Medical Assistant design during this Pandemic Like COVID-19

RESULTS

We can conclude that this system gives the accurate result.

As we are using a large dataset which will ensure better performance.

Thus we build up a system which is useful for people or hospitals to help the users to freely ask medical doubts and concerns over text. System will get output for the disease.

Users can get related answers displayed on software and refer it for analysis.

ISSN: 2456-2319 https://dx.doi.org/10.22161/eec.63.5

IV.

CONCLUSION

In conclusion the chatbot or healthcare bot was successfully able to understand user/patients healthcare related queries and lead the conversation to final diagnosis by an effective text based diagnostic technique. It applied various Machine Learning algorithms in background for effective text analysis. As a future scope we are working on adding more intents and better specification of entities to cover more symptoms and to make chatbot able to diagnose more diseases.

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V.

Medical Assistant design during this Pandemic Like COVID-19

ACKNOWLEDGEMENT

This work has been carried out by a team of 4 students, studying Engineering in Computer Science. Under the Guidance of Mr. Loveleen Kumar at Global Institute of Technology, Jaipur affiliated to R.T.U. We would like to thank Mr. Loveleen Kumar for his support and motivation to publish this paper. We will also like to thank various other individuals without whom this collaboration and Research could not have been possible.

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