Disease Prediction: Smart Disease Prediction System using Random Forest Algorithm

People nowadays suffer from a variety of diseases as a result of their living habits and the state of the environment. As a result, predicting sickness at an early stage becomes a crucial task. A doctor's ability to establish accurate diagnosis solely on symptoms, on the other hand, is restrict...

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Bibliographic Details
Published in:	2021 IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) pp. 48 - 51
Main Authors:	Swarupa, A.N.V.K, Sree, V.Hema, Nookambika, S., Kishore, Y. Kiran Sai, Teja, U. Ravi
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-11-2021
Subjects:	disease prediction Forestry Intelligent systems Machine learning algorithms Medical diagnosis Prediction algorithms random forest Random forests Task analysis
Online Access:	Get full text
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Summary:	People nowadays suffer from a variety of diseases as a result of their living habits and the state of the environment. As a result, predicting sickness at an early stage becomes a crucial task. A doctor's ability to establish accurate diagnosis solely on symptoms, on the other hand, is restricted. For the prevention and treatment of illness, an accurate and timely examination of any health-related problem is critical and challenging. In the case of a critical illness, the conventional method of diagnosis may not be adequate. There will be a huge requirement for Automated Disease Prediction System that will reduce these challenges. Developing a medical diagnosis system based on the Random Forest machine learning algorithm for disease prediction can aid in a more accurate diagnosis than the conventional way. The goal of constructing a classification system using a machine learning algorithm i.e Random Forest will substantially enable physicians in anticipating and detecting diseases at an early stage, greatly assisting in the resolution of health-related issues. For the analysis, a sample of 4920 patient records with 41 disorders was chosen. A total of 41 diseases made up the dependent variable. We enhanced 95 of the 132 independent variables (symptoms) that are closely related to illnesses. This paper illustrates a disease prediction system constructed using the Random Forest Machine Learning algorithm. Experiments were conducted with a standard symptoms dataset, and this model achieved 95 % classification accuracy. Machine learning and the Python programming language with the Tkinter Interface were used to create this disease prediction using Random Forest.
DOI:	10.1109/ICISSGT52025.2021.00021