An Integrated AI Model for Multi-Disease Prediction with Focus on Heart, Diabetes, Kidney, Liver, and Stroke

If chronic diseases are detected at an early stage, the whole process of treatment will be much effective and less costly. The problem, however, is finding a reliable method of early detection. The present work detailed in this paper is conceiving a single, tangible framework to foresee cardiac issues, diabetes, liver disorder, stroke, and chronic kidney disease, using supervised machine learning, especially Random Forest classifiers, as it has been observed that these are the most stable with clinical tabular data. This study proposes a methodology that can operate successfully with different datasets. The pipeline adopted ensures the stages of preprocessing, feature engineering, train-test split, model training, and evaluation through accuracy and confusion matrices. The models were realized in Python using pandas, NumPy, scikit-learn, and other visualization libraries, with TensorFlow being installed for any upcoming deep learning extensions. The datasets are CSV files obtained from public sources, and these files are most common for ML education and research; the output, in this case, was binary or multiclass labels for each disease type. The results manifested in this paper show that Random Forest baselines have very good predictions in all five diseases, with diabetes and kidney disease models also being given with confusion matrices and classification reports for comprehensive analysis. This work provides the research community with a reusable approach, full pipeline code, model designs, algorithms, and practical considerations, such as software needed, datasets, and diagrams of the logic of the decision. The paper can be presented at an academic conference and can be the first step in a reproducible real-world application.