Predicting the Onset of Lifestyle Diseases Using Machine Learning Techniques

Lifestyle diseases, including cardiovascular conditions, obesity, type 2 diabetes, and hypertension, are increasing global health concerns, particularly in developing countries. These non-communicable diseases are often attributed to daily habits such as poor diet, lack of exercise, smoking, and alcohol consumption. Early detection of these diseases is crucial due to their slow onset and the complexity of treatment once they manifest. This study explores the application of machine learning algorithms—such as decision trees, random forests, support vector machines (SVM), K-nearest neighbors (KNN), and Naive Bayes—in predicting the onset of lifestyle diseases. By utilizing key lifestyle factors as input data, the study evaluates the accuracy, precision, and recall of these models in diagnosing lifestyle diseases. The Random Forest algorithm emerged as the top performer with 88% accuracy. The results highlight the potential of machine learning to improve early disease detection, which could enable timely medical intervention and help reduce the long-term impact of lifestyle diseases on healthcare systems.