AI Powered Predictive Analytics in Healthcare: Leveraging Machine Learning for Early Disease Detection

Abstract

Predictive analytics have now become one of the most disruptive technologies in the healthcare industry that has been led by artificial intelligence (AI) and machine learning (ML). Unlike more traditional methods, predictive analytics is to a large extent founded on forecasting disease development and disease progression through the application of complex techniques foundational to data. The article will discuss how AI-based predictive analytics can be employed in order to ensure that diseases such as cancer, diabetes, cardiovascular diseases, and neurodegenerative diseases can be identified in their initial stages. Machine learning algorithms, including supervised and non-supervised versions, can be used to train predictive models using real-world patient data, to forecast subtle health trends that would otherwise not have been observed during traditional clinical diagnosis. It was systematic through the secondary data review of the published researches (20152024) where AI algorithms, such as logistic regression, random forests, deep learning, and natural language processing, were examined. Results highlight the reality that AI-based solutions are becoming increasingly accurate, sensitive, and specific as compared to traditional diagnostic techniques. Biasness in data, failure to interpret the model, and patient data security are also revealed in the discussion. Predictive analytics, however, has high potential of transforming healthcare to preventive, proactive, and cost effective model. Federated learning, wearable technologies, and real-time monitoring are highly likely to be utilized in the clinical setting in the future due to their further development.