Advanced Machine Learning Strategies for Chronic Disease Prediction with Effective Data Preprocessing

Predicting and detecting these illnesses early can greatly enhance patient outcomes and lessen the cost of healthcare. In order to improve predictive accuracy, this study suggests a machine learning-based approach for predicting chronic diseases that places a strong emphasis on reliable data preprocessing methods. To maximize model performance, the dataset is subjected to categorical encoding, feature scaling, and missing value imputation. Numerous health-related factors, including age, BMI, blood pressure, cholesterol, blood sugar, smoking, exercise frequency, kidney and lung diseases, family history, and obesity, are used to train a Random Forest Classifier. Real-time disease prediction based on user-input health metrics is then made possible by the deployment of the trained model as a Flask-based web application. Accuracy, precision, recall, and F1-score are performance evaluation measures that demonstrate how well the model identifies persons at risk of developing chronic illnesses. This technology could help people and medical professionals monitor their health proactively, which would help avoid sickness and promote early intervention. Keywords: Chronic Disease Prediction, Machine Learning, Data Preprocessing, Random Forest Classifier, Healthcare Analytics, Flask-based Web Application