Predictive Analytics in Healthcare: A Machine Learning Model for Early Health Risk Detection

Abstract

The contemporary healthcare landscape is undergoing a paradigm shift, moving from a reactive, treatment-centric model to a proactive, prevention-oriented approach. Central to this transformation is the application of predictive analytics powered by machine learning (ML). This paper investigates the development and implementation of ML models for the early detection of health risks associated with chronic conditions such as cardiovascular disease and diabetes. By leveraging heterogeneous data sources—including electronic health records (EHRs), demographic information, and real-time biometric data—these models can identify subtle, complex patterns that often elude conventional clinical analysis. We synthesize the current state-of-the-art in predictive modeling, discussing a range of algorithms from logistic regression and random forests to advanced deep learning architectures like recurrent neural networks. The analysis critically addresses the significant challenges impeding widespread clinical adoption, including data quality and interoperability, algorithmic interpretability, and pervasive model bias. Furthermore, the paper examines the ethical imperatives of data privacy and the necessity for robust regulatory frameworks. Ultimately, this research posits that while ML-driven predictive analytics holds immense potential to revolutionize preventive care and improve patient outcomes, its successful integration into clinical workflows hinges on overcoming these multifaceted technical and ethical hurdles to build trustworthy, equitable, and actionable decision-support systems.