An Experimental Analysis of Multiple Disease Prediction Using Machine Learning Algorithms

The development of smart healthcare tools is progressing at a rapid pace. It is only a matter of time when the healthcare system all over the world becomes completely automated. This paper contains research conducted on datasets of four different disease being Diabetes, Kidney Disease, Liver Disease and Asthma in a hope to accelerate the growth of machine learning models in the medical world by achieving the most optimal model possible. The models used in the research were Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), K-Neighbour, SVC, AdaBoost and MLP. Random Forest performed best for every dataset achieving mean accuracy score of 87.85% for Diabetes, 95.8% for Kidney disease, 99.68% for Kidney Disease and 95.83% for Asthma.