Analyzing Genetic Data with Machine Learning to Identify Disease Risk

The rapid advancement of genomic technologies has resulted in vast amounts of genetic data, offering unprecedented opportunities for disease risk prediction and personalized medicine. However, the high dimensionality and complexity of genetic datasets pose significant challenges in identifying meaningful patterns and associations. This study explores the application of Machine Learning (ML) techniques to analyze genetic data for the identification of disease risk factors. We investigate both traditional algorithms, such as Support Vector Machines (SVM) and Random Forests, and more advanced Deep Learning (DL) approaches, including Convolutional Neural Networks (CNN), to evaluate their effectiveness in detecting genomic variants linked to various diseases. A publicly available genetic dataset is employed to train and validate the models, with performance assessed using metrics such as accuracy, precision, recall, and AUC-ROC. Our findings demonstrate that ML methods, particularly ensemble and DL models, significantly enhance the ability to predict disease susceptibility from complex genetic information. This research highlights the transformative potential of ML in genomic medicine and provides a foundation for future work aimed at improving disease prevention and early diagnosis strategies.

کلیدواژه ها:

Genetic Data ، Machine Learning ، Disease Risk Prediction ، Genomic Analysis ، Feature Selection ، Personalized Medicine