Machine Learning-Based Genetic Disorder Prediction: The Role of Ensemble Models in Improving Accuracy

Author(s)	Mr. Ezra Yalley
Country	India
Abstract	Genetic disorders pose significant diagnostic challenges, often needing costly and invasive genetic tests that might not be available in places with limited resources. This study investigates the effectiveness of ensemble learning in genetic disorder prediction, highlighting its advantages over traditional machine learning models. Several models including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Neural Networks, Random Forest, XGBoost, CatBoost, and AdaBoost were trained and evaluated. Results demonstrated that ensemble models (XGBoost, CatBoost, AdaBoost, and Random Forest) significantly outperformed non-ensemble models in predicting genetic disorders. To further enhance predictive accuracy, the best-performing ensemble models were combined using a stacked ensemble approach, achieving an improved accuracy of 96.19%. These findings show that ensemble learning is superior for predicting genetic disorders offering a more accurate and accessible tool for diagnosis, especially in healthcare settings with limited resources.
Keywords	Artificial Intelligence, Machine Learning, Healthcare AI, Genetic Disorder Prediction, Ensemble learning, Data Science.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-14
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.46791
Short DOI	https://doi.org/g9qp49

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