Chronic Disease Prediction through Supervised Learning Techniques

Abstract

About 10.5 percent of global adult population is living with diabetes. India has 77 million diabetic patients, it is the second highest in the
world. Developing countries such as India face a huge burden of diabetes and its complications. Even children at the age of five are suffering
from this disease. It is high time that people understand the gravity of the situation and make themselves fit to fight the disease than to suffer
with it. If diabetes is not identified and treated in right time, it may lead to chronical health issues. In this paper a machine learning based
prediction model is built to find the factors leading to complicated health issues such as cardio vascular disease. This model identifies the
attributes that highly contribute to cardio vascular disease and compare various machine learning algorithms to predict cardio vascular
disease among diabetic patients. It identifies the best algorithm from a set of supervised learning algorithms such as KNN, Decision Tree,
Random Forest, Naïve Bayes and Gradient Boosting for prediction based on several performance metrics. The algorithms are compared
based on the performance metrics such as accuracy, precision, recall, F1 score, time taken to train and time taken to test. We identified that
Decision Tree with entropy as the split criterion achieved the highest accuracy.