Integration of Machine Learning in Predictive Health Diagnostics

Katu Amina H.

School of Natural and Applied Sciences Kampala International Uganda

ABSTRACT

The integration of machine learning (ML) into predictive health diagnostics is revolutionizing healthcare by enabling early detection, personalized treatment, and resource optimization. By leveraging large datasets, advanced algorithms, and interdisciplinary collaborations, predictive diagnostics empower healthcare providers to identify risks and manage diseases proactively. This paper investigates the fundamentals of predictive health diagnostics and ML, emphasizing their applications in disease prediction, risk stratification, and personalized medicine. It also addresses challenges such as data quality, privacy, and ethical considerations, offering solutions to foster responsible implementation. Emerging trends, such as real-time analytics and wearable-driven monitoring, are discussed, highlighting the potential for a paradigm shift toward preventive healthcare. The findings emphasize the necessity of a collaborative effort among technologists, clinicians, and policymakers to overcome barriers and harness the transformative potential of ML in predictive health diagnostics.

Keywords: Predictive health diagnostics, Machine learning, Artificial intelligence, Personalized medicine, Disease prediction.

CITE AS: Katu Amina H. (2024). Integration of Machine Learning in Predictive Health Diagnostics. RESEARCH INVENTION JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES 4(3):1-7. https://doi.org/10.59298/RIJSES/2024/4317