A Comparative Evaluation of Machine Learning Architectures for Early Multi-Disease Prediction and Clinical Diagnostics
Keywords:
Classification, Data mining, Disease prediction, Health care, Machine learning, Neural network, Random forest, SVMAbstract
Early disease detection is very important in modern healthcare because many serious illnesses are diagnosed only after they become severe. Diseases such as heart disease, diabetes, cancer, and kidney disease are responsible for millions of deaths every year across the world. Traditional medical diagnosis methods are often time-consuming, expensive, and sometimes unable to identify diseases at an early stage. Because of this, researchers are now focusing on machine learning techniques to help doctors make faster, more accurate decisions. This study examines the role of machine learning in predicting diseases using patient health data. Different machine learning models, including Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Gradient Boosting, and Neural Network, were applied and compared. Publicly available healthcare datasets were used for model training and testing. Before model development, data preprocessing methods such as handling missing values, normalization, and feature selection were performed to improve prediction quality. The models were evaluated using accuracy, precision, recall, and F1-score measures. The experimental results showed that the Neural Network model achieved the highest prediction accuracy of 93.1%, while Gradient Boosting and Random Forest also produced strong results with accuracies of 90.7% and 89.4%, respectively. The study demonstrates that machine learning can become an effective support system for early disease diagnosis and may help healthcare professionals provide timely treatment and reduce mortality rates.
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