Chronic Disease Prediction Using Different ML Techniques

These days, emerging technologies like artificial intelligence (AI)is crucial to the developments in the medical field. Machine learning will become very helpful for experts in making their decisions fast, accurate and early disease predictions. Early disease prediction is very much needed for an easy cure and reducing the mortality rate for that specific disease. Machine Learning has various techniques for the prediction of different diseases, like some chronic diseases such as cardiovascular disease, Diabetes, Dementia, Cancer and Kidney failure. In this research study five different machine learning techniques, i.e. Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Gradient Boosting Model(GB) and eXtreme Gradient Boost (XGB) are analyzed for the mentioned diseases. For this analysis some parameters like Precision, Recall, f-Score, accuracy and Time complexity are considered. In the experimental setup the overall dataset is divided into two parts which is 70:30 as training and testing dataset. So the results obtained from the given experimental model show that the GB classifier will gives the most optimized results for heart disease prediction, LR gives a good result for diabetes prediction and XGBoost is best for dementia prediction while the time complexity is optimized for LR and DT.