Enhancing Multi-Disease Prediction with Machine Learning: A Comparative Analysis and Hyperparameter Optimization Approach

Although traditional methods based on statistical parameters are still important in healthcare, Machine learning (ML) algorithms offer promising results for analyzing health data. Therefore, the presented work aimed to evaluate the success of several supervised ML models with hyperparameter optimization (HPO) for predicting multiple diseases such as diabetes, heart disease, Parkinson's disease, and breast cancer. We evaluated seven distinct algorithms: Logistic Regression (LR), Gradient Boosting (GB), k-Nearest Neighbors (k-NN), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), Random Forests (RF), and a basic "nonlinear mapping technique". Each algorithm was trained and compared in isolation for each targeted health condition. The success of these techniques was assessed using standard performance metrics like accuracy, precision, F1-score, and recall. Additionally, hyperparameter optimization was applied to each algorithm and its effect on the result was observed. The results show the potential of ML for multiple disease prediction with individual models achieving high accuracy for specific diseases. SVM achieved 100% accuracy for heart disease, Gradient Boosting achieved 90% for diabetes, a simple Neural Network achieved 99% for breast cancer, and Random Forest achieved 100% for Parkinson's disease. These results emphasize the importance of selecting appropriate models for specific disease prediction tasks. A web-based application has been developed so that users can easily use the models by selecting a disease, providing relevant input, and receiving a prediction based on the chosen model. In conclusion, this study highlights the potential of machine learning and hyperparameter optimization for multi-disease prediction and underlines the importance of model selection.