Health Prediction using Machine Learning Methodologies

Adaptable Critical Patient Caring system is a vital worry for medical clinics in agricultural nations like Bangladesh. A large portion of the clinic in Bangladesh need serving legitimate wellbeing administration because of inaccessibility of suitable, simple and adaptable brilliant systems. The point of this undertaking is to fabricate a sufficient system for medical clinics to serve critical patients with a constant input technique. In this paper, we propose a nonexclusive engineering, related phrasing and a classificatory model for noticing critical patient's ailment with machine learning and IBM distributed computing as Platform as an assistance (PaaS). Machine Learning (ML) based wellbeing forecast of the patients is the critical idea of this examination. IBM Cloud, IBM Watson studio is the stage for this examination to store and keep up with our information and ml models. For our ml models, we have picked the accompanying Base Predictors: Naïve Bayes, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and MLP Classifier. For working on the precision of the model, the packing technique for outfit learning has been utilized. The accompanying calculations are utilized for group learning: Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC, and Bagging Ridge. We have fostered a versatile application named "Critical Patient Management System - CPMS" for constant information and data see. The system engineering is planned so that the ml models can prepare and send in a continuous stretch by recovering the information from IBM Cloud and the cloud data can likewise be gotten to through CPMS in a mentioned time span. To help the specialists, the ml models will foresee the state of a patient. On the off chance that the forecast dependent on the condition deteriorates, the CPMS will send a SMS to the obligation specialist and medical caretaker for standing out enough to be noticed to the patient. Consolidating with the ml models and portable application, the undertaking may fill in as a keen medical services answer for the emergency clinics