Health Prediction Using Machine Learning with Drive HQ Cloud Security
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Abstract
Focusing on the making of a framework that tends to lacks in healthcare through an emphasis on responsiveness and versatility is fundamental. The point is to upgrade medical clinic administration conveyance by further developing therapy for basically sick patients. Machine learning and cloud-based “platform as a service (PaaS)” technologies advances should be utilized to screen the medical issue of basic patients continuously. The essential spotlight is on decision-making and monitoring capabilities inside the medical care industry. Eminently, the IBM Cloud part is imitated locally to sidestep cost imperatives. A model consolidating a gathering procedure that incorporates "Random Forest (RF), Logistic Regression (LR), and Gradient Boosting (GB)" is used. This strategy utilizes machine learning methods including “Naïve Bayes and Decision Tree Classifier”. This technique expects to lay out a system that is strong and versatile for expecting critical medical problems. The "Critical Patient Management System (CPMS)” portable application should be worked to real-time remote monitoring of patient circumstances. The application means to furnish clinical experts with proficient apparatuses for medical care organization, permitting them to monitor basically sick patients.