Machine learning system for predicting genetic disorders using genomic, clinical, and demographic data. Implements robust preprocessing, feature selection, and multi-model classification (RF, XGBoost, LightGBM, CatBoost) with cross-validation to support early, data-driven genetic risk assessment.
machine-learning data-mining bioinformatics genomics random-forest xgboost lightgbm ensemble-learning academic-project data-preprocessing feature-engineering predictive-modeling imbalanced-data model-evaluation precision-medicine catboost applied-data-science classification-models medical-ai healthcare-analytics
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Updated
Jan 2, 2026 - JavaScript
