Predictive Modeling in Healthcare Using Intelligent Data Analytics for Early Disease Detection

Abstract

Predictive analytics in healthcare has emerged as one of the mostrevolutionary applications of intelligent data technologies. With the rapidincrease in medical records, diagnostic images, genetic sequences, andlifestyle data, it has become critical to utilize predictive algorithms to identifyearly warning signs of diseases. This paper explores the integration ofpredictive modeling techniques such as logistic regression, decision trees, and deep learning for identifying chronic illnesses including cardiovasculardisease, diabetes, and cancer at an early stage. A significant focus is given to the data preprocessing steps, handling of imbalanced datasets, andinterpretability of machine learning models in clinical decision-making. Thestudy also highlights the role of personalized medicine, where intelligent data frameworks provide patient-specific predictions by combining clinical,genetic, and environmental factors. By demonstrating the potential ofpredictive analytics in preventive medicine, this research emphasizes thatpredictive healthcare can substantially reduce hospitalization costs, improvelife expectancy, and increase the efficiency of clinical workflows. KEYWORDS: Predictive analytics, Intelligent data, Healthcare modeling,Early disease detection, Clinical decision support