Decoding the Genome with AI: A Machine Learning-Based Predictive Model for the Diagnosis and Prognosis of Genetic Disorder

Medical challenges for genetic disorders abound in DNA variation causing some expression in the sufferer. Based on this, this paper presents a novel machine learning framework for predicting and classifying genetic disorders making use of supervised and unsupervised learning methods along with the multi-omics data. To address the issues of genomic data processing, namely high-dimensionality, feature selection, and data heterogeneity, advanced SVM, Random Forest, and Neural Network (NN) classifiers are proposed within the framework. It is done by utilizing the ensemble learning methods to improve prediction accuracy and system stability, which outperforms the traditional method. The framework shows high F1 scores through data preprocessing and feature extraction for genomic datasets, which show its possible capability of giving more accurate and practical results concerning personalized treatment. We will focus on future work that aims to increase the diversity within the dataset, scale the system to become applicable on a broader scale and integrate other omics layers to better enhance the prediction capability as well as enable real time clinical decision making. The results of this research relate the importance of the application of machine learning in revolutionizing genetic medicine, through early diagnosis and personalized care. deep learning approaches and multi-omics data.