Predictive AI Healthcare Analytics for Early Disease Detection Leveraging AI and Machine Learning Models to Identify High-Risk Patients and Improve Preventive Healthcare Strategies

Abstract

With an increase in complexities in the field of healthcare resulting from issues like aging populations, increasing healthcare treatment costs, a shortage of workers and the high number of patients suffering from chronic diseases, predictive artificial intelligence is increasingly becoming important in the future of healthcare. In this article, the author examines the role played by the use of AI predictive healthcare analytics and machine learning methods in helping to identify risks associated with patients and early detection of disease risks. This research is conducted in a secondary research methodology in the form of a literature review of the existing studies on the subject. The paper evaluates several AI machine learning algorithms including logistic regression, decision trees, random forest, support vector machines, gradient boosting, neural networks, deep learning, NLP (natural language processing) and ensemble machine learning models. These AI algorithms are suggested to assist healthcare professionals in understanding the risks of disease among their patients through EHR (electronic health record), lab tests, imaging, devices, lifestyle data and genomics. Advantages of AI include clinical decision making, personalized prevention, improved patient outcome, reduction in hospital admission rates and better allocation of resources. However, there is a question concerning the privacy of information, discrimination through algorithms, non-explainability, false-positive predictions, false-negative predictions, the clinical validity of the tool, and accountability. In conclusion, the author states that predictive AI should be responsibly utilized and support rather than replace health care specialists.