Machine Learning Revolution in Early Disease Detection for Healthcare: Advancements, Challenges, and Future Prospects
Kumbala Pradeep Reddy, Mankala Satish, Allam Jaya Prakash, S. Malli Babu, Praveen Kumar, B. Sunitha Devi
Abstract
This paper explored the integration of machine learning into healthcare has revolutionized early disease detection, offering a multidimensional approach to data analysis. Advanced algorithms, rooted in deep learning, process diverse datasets encompassing medical records, genetics, and imaging data, enabling subtle pattern detection. Deep learning, predictive analytics, natural language processing, anomaly detection, and personalized medicine have ushered in a proactive healthcare era, leading to better patient outcomes, reduced misdiagnosis, and cost-effective treatments. Systematic reviews underscore machine learning's impact across various medical domains. The future holds promise with enhanced data integration, interdisciplinary collaboration, explainable AI, real-time monitoring, global healthcare accessibility, ethical considerations, and continuous learning, ultimately reshaping healthcare for the better.