Early Health Prediction System for ICU Patient using Machine Learning and Cloud Computing

EasyChair Preprint 1590

Abstract

Adaptable Intensive Care Unit (ICU) system is a key concern for hospitals in developing countries like Bangladesh. Most of the hospital in Bangladesh lack serving proper health service due to unavailability of appropriate, easy and scalable systems. There also appears communication and information gaps between hospital authority and patient’s relative. The aim of this project is to build an adequate system for hospitals to serve the ICU patients with a real-time feedback system. Based on the doctor’s suggestions, we have primarily chosen the main factors for our project. In this paper, we propose a generic architecture, associated terminology and a classificatory model for observing ICU patient’s health condition with machine learning and cloud computing. Machine learning (ML) health prediction is the key concept of this research. IBM Cloud is the platform for this research to store and maintain our data. For our ml models, we have chosen the following algorithms: Naive Bayes, Logistic Regression. For real-time data and information view, we have developed a Mobile Application named “ICU Patient Management System - IPMS”. Our system architecture is designed in such a way that the ml models can train and deploy in a real-time interval by retrieving the data from IBM Cloud and the cloud information can also be accessed through IPMS in a requested time interval. To help the doctors, the ml models will predict the condition of a patient. If the prediction based on the condition gets worse, the IPMS will send an SMS to the duty doctor and nurse for getting immediate attention to the patient. Combining with the cloud storage, distributed database system, ml models and mobile application, the project may serve as a complete medical decision for the doctors.

Keyphrases: Cloud Computing, ICU Patient Monitoring System, machine learning

@booklet{EasyChair:1590,
  author    = {Asif Ahmed Neloy and Muhammad Shafayat Oshman and Md. Monzurul Islam and Md Julhas Hossain and Zunayeed Bin Zahir},
  title     = {Early Health Prediction System for ICU Patient using  Machine Learning and Cloud Computing},
  howpublished = {EasyChair Preprint 1590},
  year      = {EasyChair, 2019}}