Machine Learning-Based Predictive Models for Early Detection of Chronic Diseases

Abstract

The prevalence and the long-term nature of chronic diseases such as cardiovascular disorders, diabetes, cancer, and osteoporosis are a major challenge to the health of the world because they are highly prevalent and have long-term effects. This requires careful prediction and early identification in order to minimize morbidity and revise patient outcomes. In this paper, the author discusses machine learning as a predictive model and early chronic disease diagnosis. There were numerous supervised and unsupervised learning algorithms that were utilized to process largescale patient datasets and electronic health records, such as decision trees, random forests, support vector machine, neural networks, and ensemble methods. To improve the performance of the models and their interpretability, a feature selection and data preprocessing were performed. Findings indicated that machine learning models are capable of high predictive accuracy, sensitivity, and specificity in predicting at-risk persons and disease progression. The combination of wearables, deep learning models and predictive analytics enhanced more personal risk assessment and intervention plans. The results demonstrate the possible role of AI-based diagnostic systems to facilitate clinical decision-making, facilitate timely interventions, and streamline the allocation of healthcare resources. Future studies need to increase data size, enhance predictability of modes and incorporate multi-modal health data to achieve greater predictive quality. The paper highlights the disruptive nature of machine learning in the chronic disease management and preventive healthcare.