Disease Prediction From Various Symptoms Using Machine Learning

7 Pages Posted: 8 Oct 2020

See all articles by Rinkal Keniya

Rinkal Keniya

K. J. Somaiya College of Engineering

Aman Khakharia

K. J. Somaiya college of engineering

Vruddhi Shah

University of Mumbai - K. J. Somaiya College of Engineering (K.J.S.C.E.)

Vrushabh Gada

K. J. Somaiya College of Engineering

Ruchi Manjalkar

K. J. Somaiya College of Engineering

Tirth Thaker

K. J. Somaiya College of Engineering

Mahesh Warang

K. J. Somaiya College of Engineering

Ninad Mehendale

K. J. Somaiya college of Engineering; Ninad's research Lab

Date Written: July 27, 2020

Abstract

Accurate and on-time analysis of any health-related problem is important for the prevention and treatment of the illness. The traditional way of diagnosis may not be sufficient in the case of a serious ailment. Developing a medical diagnosis system based on machine learning (ML) algorithms for prediction of any disease can help in a more accurate diagnosis than the conventional method. We have designed a disease prediction system using multiple ML algorithms. The data set used had more than 230 diseases for processing. Based on the symptoms, age, and gender of an individual, the diagnosis system gives the output as the disease that the individual might be suffering from. The weighted KNN algorithm gave the best results as compared to the other algorithms. The accuracy of the weighted KNN algorithm for the prediction was 93.5 %. Our diagnosis model can act as a doctor for the early diagnosis of a disease to ensure the treatment can take place on time and lives can be saved.

Keywords: Disease Prediction, Machine Learning, Symptoms

JEL Classification: I

Suggested Citation

Keniya, Rinkal and Khakharia, Aman and Shah, Vruddhi and Gada, Vrushabh and Manjalkar, Ruchi and Thaker, Tirth and Warang, Mahesh and Mehendale, Ninad and Mehendale, Ninad, Disease Prediction From Various Symptoms Using Machine Learning (July 27, 2020). Available at SSRN: https://ssrn.com/abstract=3661426 or http://dx.doi.org/10.2139/ssrn.3661426

Rinkal Keniya

K. J. Somaiya College of Engineering ( email )

India

Aman Khakharia

K. J. Somaiya college of engineering ( email )

India

Vruddhi Shah

University of Mumbai - K. J. Somaiya College of Engineering (K.J.S.C.E.) ( email )

Mumbai, MA Maharashtra 400007
India

Vrushabh Gada

K. J. Somaiya College of Engineering ( email )

India

Ruchi Manjalkar

K. J. Somaiya College of Engineering ( email )

India

Tirth Thaker

K. J. Somaiya College of Engineering ( email )

India

Mahesh Warang

K. J. Somaiya College of Engineering ( email )

India

Ninad Mehendale (Contact Author)

K. J. Somaiya college of Engineering ( email )

Mumbai, MA Maharashtra 400007
India

Ninad's research Lab ( email )

M.G. Road, Naupada Thane
Thane, 400602
India

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