Early Disease Diagnosis using Cloud Computing-Powered Machine Learning on Health Records

Modern approaches to disease detection have become possible because EHRs continue to increase in quantity along with improvements in machine learning (ML) applications using cloud computing. The proposed research presents a cloud-supported machine learning framework which uses heterogeneous health data at scale for establishing rapid disease predictions. The system supports efficient data processing and real-time analytics along with secure patient record storage through its combination of scalable cloud infrastructure with intelligent ML models. The research identifies decision trees along with support vector machines and deep neural networks as key supervised learning algorithms which demonstrate the best methods for detecting diseases. Model performance increases through implementation of feature selection along with data preprocessing techniques which also guarantee secure protection for sensitive health-related information. Results from experiments prove that ML models hosted in the cloud improve diagnosis precision and system performance better than conventional techniques. The proposed design enables remote healthcare monitoring together with ongoing surveillance that brings value to proactive medical service delivery while decreasing clinical staff responsibilities. The research shows how healthcare benefits from uniting cloud computing and ML as this system delivers efficient and precise care through standard and economical disease detection solutions.