GenChrome-ML: A Machine Learning Framework for Early Detection of Chromosomal Disorders Using Genomic Data - Anjali Rout (International Journal of Bioinformatics and Computational Biology, 2026)
The increasing burden of chronic disease and cancer demands innovative, more rapid and effective diagnostic tools in the field of healthcare. The majority of current diagnostic tools are dependent upon clinical symptomology and manual evaluation, leading to delays in early detection and treatment. The development of artificial intelligence (AI) and machine learning (ML), in recent years, has offered opportunities for the enhancement of disease prediction, diagnosis and personalization of treatment plans in healthcare.This paper will critically evaluate and analyze previous research studies investigating applications of AI in healthcare specifically within the realm of chronic disease prediction, cancer diagnosis, stroke detection, and personalized medicine.The methodology for this study was based on a comprehensive review of previous research articles focused on AI and its applications in medical diagnosis and predictive healthcare systems. A comparative analysis of these studies was completed to identify various techniques applied in their research, including machine learning algorithms, biomarker analysis, genomic data analysis, digital pathology, and multimodal health monitoring systems. Additionally, emphasis was placed on studies that utilized a combination of medical data including clinical record data, imaging data, wearable sensor data and/or genetic data. Comparative results from the studies reviewed indicate that AI based methodologies outperform traditional diagnostic approaches in both early detection and predictive accuracy. Machine learning techniques, including support vector machines, deep neural networks, and hybrid analytical approaches, have been shown to enhance prediction accuracy in various studies. Although there has been significant progress in the application of AI in healthcare, challenges with respect to data protection, model transparency and data quality remain. Therefore, the study demonstrates that AI has great potential to enhance the quality of disease diagnosis, to aid clinicians in making decisions regarding care and to develop more personalized healthcare options in the future.
Keywords: Chromosomal Abnormality Detection; Early Genetic Disorder Diagnosis; Machine Learning Algorithms; Multimodal Biomedical Data Integration; artificial intelligence; predictive analytics