The Early Prediction of Chronic Diseases Through Diverse Machine Learning Techniques

Advancements in healthcare analytics offer significant benefits for both healthcare providers and patients. The application of analytics in healthcare plays a crucial role in early disease detection and diagnosis, contributing to enhanced healthcare quality and improved patient outcomes. Machine learning models play a pivotal role in identifying patterns within data, enabling the generation of predictions for disease diagnosis, prognosis, and treatment. With continuous advancements in algorithms and technology, these models have become increasingly effective in providing optimal patient care. This research focuses on utilizing various machine learning algorithms to predict the diagnosis of chronic disease. The study aims to assess and compare the effectiveness of these models by evaluating their prediction accuracy, as well as other performance metrics such as precision, recall, and F1 score. Four different chronic disease datasets considered which includes diabetes, stroke, cardiovascular disease, and Chronic Kidney Disease. Among the investigated models, Random Forest emerged as the most effective, achieving an impressive accuracy rate of 70 to 99% depends on the dataset.