• Anchal Gupta
• Ankit Shah
• Farhan Yezdani
• Chakshu Chawla

A well-targeted and timely analysis is the answer to all health-related issues. However, for serious diseases such as Diabetes and Heart disease, traditional methods may not be adequate. Therefore, we have developed a disease prediction model based on Machine Learning [ML] algorithms for Diseases like Diabetes and Heart Disease. The dataset we have developed is a variable dataset which includes factors such as Age, Gender, Height, Weight etc. for the analysis of a specific disease to get a diagnosed output. For Diabetes , the Random Forest algorithm gives the best results with 84% accuracy. For Heart Disease , the SVM algorithm provides the best results with 86% accuracy. Our disease prediction model can be a virtual doctor in the future for timely prediction so that the patient can receive timely treatment and live a longer life.

Disease prediction, Machine Learning, Algorithms

"E - Chikitsaleya", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.h744-h755, May-2024, Available :http://www.jetir.org/papers/JETIR2405794.pdf

"E - Chikitsaleya", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pph744-h755, May-2024, Available at : http://www.jetir.org/papers/JETIR2405794.pdf