Predictive Analytics for Personalized Healthcare: Integrating Clinical History and Genomic Features for Treatment Effectiveness Prediction

Predictive analytics offers a promising approach for personalized healthcare by combining patient-specific clinical information with genomic characteristics. The proposed framework covers data collection, preprocessing, feature engineering, predictive modeling, validation, explainability, and deployment.   The implemented Flask prototype demonstrates the concept through logistic regression using Age, BloodPressure, Glucose, and GeneScore, with probability output and prediction history. The distinction between proposed methodology and implemented prototype is important: advanced modeling and clinical-integration components are future extensions rather than demonstrated results.   The work provides a practical foundation for treatment-effectiveness prediction while emphasizing that clinical deployment requires high-quality data, external and prospective validation, privacy protection, interpretability, fairness assessment, and collaboration with healthcare professionals.

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