Disease Diagnosis Interface Using Machine Learning Technique

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Author(s): D. Rajeswari (Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM institute of Science and Technology, Kattankulathur, India), Athish Venkatachalam Parthiban (Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India)and S. S. Sree Nandha (Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India)
Copyright: 2023
Pages: 10
Source title: Perspectives on Social Welfare Applications’ Optimization and Enhanced Computer Applications
Source Author(s)/Editor(s): Ponnusamy Sivaram (G.H. Raisoni College of Engineering, Nagpur, India), S. Senthilkumar (University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, India), Lipika Gupta (Department of Electronics and Communication Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, India)and Nelligere S. Lokesh (Department of CSE-AIML, AMC Engineering College, Bengaluru, India)
DOI: 10.4018/978-1-6684-8306-0.ch009

Keywords: Engineering Science Reference / Human Aspects of Technology / Social Sciences & Humanities / Systems and Software Engineering

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Abstract

Self-care has acquired relevance, especially in light of the COVID-19 scenario. For anyone to diagnose underlying disorders without a doctor's involvement, improved remote healthcare equipment was required. Due to recent technical breakthroughs, this mission is no longer insurmountable. The objective is to develop an interactive application that can identify potential reasons for a person's discomfort. The primary objective is to carry out a trustworthy machine learning technique that can accurately predict a person's status depending on their symptoms. The collection includes 5000 individual cases and 133 distinctive symptom types. On the same dataset, three alternative models (support vector classification, random forest and Naive Bayes) were instructed to achieve maximum accuracy. The second part involves developing a web application and integrating the model with it. The primary aim of the project is to implement a machine learning based web application that is user-friendly and easy to understand, so that patients can detect their problems before visiting a doctor.

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