Genetic Disorder Prediction Using Machine Learning and Artificial Intelligence Techniques

Abstract

Genetic disorders constitute a major global public health challenge due to their complex inheritance patterns that influence disease susceptibility, progression, and clinical outcomes. Early and accurate detection of these conditions is crucial for timely medical intervention and the development of personalized treatment strategies that enhance patient well-being. This study introduces an advanced web-based application that integrates machine learning techniques—particularly the Random Forest (RF) algorithm—to analyze genetic biomarkers in combination with comprehensive clinical and neuroimaging datasets for improved predictive accuracy. By merging multiple data modalities, the system effectively identifies intricate correlations between genetic and phenotypic features, providing reliable risk assessments for various genetic diseases. Developed using Python and the Flask web framework, the proposed platform delivers a user-friendly interface that empowers healthcare professionals and patients with real-time, data-driven insights into genetic health. The results highlight the significant potential of machine learning in advancing precision medicine, supporting proactive healthcare management, and facilitating informed clinical decision-making to improve preventive care outcomes.