Advancing Precision Healthcare: Machine Learning for Enhanced Diagnostics and Personalized Treatment

Precision healthcare has advanced substantially with the integration of machine learning (ML), enhancing diagnostic accuracy and facilitating personalized treatment approaches. This study investigates the application of three key ML models—Support Vector Machines (SVM), Random Forests (RF), and Recurrent Neural Networks (RNN)—within essential areas of patient care. Results indicate that RF delivers superior diagnostic accuracy with an accuracy rate of 93.2%, precision of 91.5%, recall of 94.0%, F1-score of 92.7%, and an AUC of 0.96, making it highly effective in handling complex clinical and genomic data for disease prediction and individualized treatment planning. SVM, with a diagnostic accuracy of 91.5%, precision of 89.8%, recall of 92.1%, and F1-score of 90.9%, also provides reliable classification, particularly for tasks involving patient profiles and genetic markers, which support early diagnosis and risk assessment. Conversely, RNN demonstrates its strength in managing chronic disease trends, achieving a Trend Accuracy of 86.7%, Prediction RMSE of 1.76, and a Time-Series AUC of 0.93, confirming its suitability for analyzing temporal health data and supporting long-term disease management. This study addresses practical challenges in deploying ML within healthcare, such as data security, ethical implications, and clinical integration, through a comprehensive evaluation of the benefits, limitations, and specific applications of these models. The proposed framework demonstrates the potential of ML to enhance patient-centered care through accurate, reliable, and customized interventions, paving the way for innovation in precision healthcare.