A Review on Personalized Treatment Recommendations using Machine Learning

Abstract

This study develops and evaluates a machine learning-based system for personalized treatment recommendations, achieving 91.2% prediction accuracy through neural network modeling. The research implements a comprehensive analytical pipeline encompassing data preprocessing (KNN imputation, SMOTE balancing), feature engineering (identifying Disease Severity Score and Patient Age as top predictors), and comparative evaluation of five machine learning algorithms. Results demonstrate statistically significant superiority (p<0.01) of neural networks over alternative approaches (Gradient Boosting: 89.7%, Random Forest: 87.3%), with strong generalizability evidenced by only 2.5% performance drop on external validation. SHAP analysis provides clinically interpretable explanations for recommendations, revealing distinct treatment patterns (e.g., 28.3% Medication Therapy A for moderate-severity patients vs. 12.4% Surgical Intervention for severe cases with 94.2% success rate). The system addresses key clinical challenges through robust feature importance analysis (55.3% weight to clinical factors) and sensitivity testing (±5% noise causing just 1.2% accuracy decline). Limitations include temporal data constraints (2018-2023 only) and lack of genomic data integration. Future directions highlight temporal modeling, multimodal data fusion, and clinical trial applications. This work bridges AI innovation with clinical decision-making, demonstrating how machine learning can enhance precision medicine while maintaining interpretability for healthcare providers. Keywords: Personalized Medicine, Machine Learning, Treatment Recommendation, Healthcare Analytics, Precision Medicine, Clinical Decision Support

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