• GARLAPATI BHUVANESWARI
• Dr.J.Rajendra Prasad
• K.Sai Sravani
• G.Bharathchadu

Adaptable Critical Patient Caring system is a key concern for hospitals in developing countries like some villages. Most of the hospital in some villages lack serving proper health service due to unavailability of appropriate, easy and scalable smart systems. The aim of this project is to build an adequate system for hospitals to serve critical patients with a real-time feedback method. In this paper, we propose a generic architecture, associated terminology and a classificatory model for observing critical patient’s health condition with machine learning and IBM cloud computing as Platform as a service (PaaS). Machine Learning (ML) based health prediction of the patients is the key concept of this research. IBM Cloud, IBM Watson studio is the platform for this research to store and maintain our data and ml models. For our ml models, we have chosen the following Base Predictors: Naïve Bayes, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier,and MLP Classifier. For improving the accuracy of the model, the bagging method of ensemble learning has been used. The following algorithms are used for ensemble learning: Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC, and Bagging Ridge.We have developed a mobile application named “Critical Patient Management System - CPMS” for real-time data and information view.

CPMS,SVC,Bagging,Logistic Regression,MLP,HER.

"Machine Learning Revolutionizes Healthcare with IBM Cloud as paas", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.e230-e252, March-2024, Available :http://www.jetir.org/papers/JETIR2403429.pdf

"Machine Learning Revolutionizes Healthcare with IBM Cloud as paas", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppe230-e252, March-2024, Available at : http://www.jetir.org/papers/JETIR2403429.pdf