Prediction of Disease Severity Using Machine Learning Algorithm
15 Pages Posted: 9 Mar 2018
Date Written: November 15, 2017
In modern health care environment, monitoring health information about the patients, physically disabled persons and people those who are in the critical stage have become a focus of recent researches and development. Identification and solving the health issues and diseases diagnosis is a most important problem now a day. The formal diagnosis model has the problem of early diseases detection. The machine-learning framework like decision tree will address the formal diagnosis model problem. The proposed Diseases Analysis and Identification System using Machine Learning (DAISML) focused on the diseases diagnosis model for early detections diseases without visiting the clinics or hospital often. The availability of medical IOT devices, it becomes feasible to collect the health contexts from personal IoT devices. The personal health information is collected frequently for a period of time and it will be maintained in a repository. Thought the collected personal health information, identify the specific disease and disease diagnosis is effectively handle the diagnosis schemes by focusing on the pattern matching-based diagnosis and machine-learning framework, it will increase the benefits of early disease detection, promotion of frequent assessments on the health conditions and the awareness of proactive healthcare essentials. Three different approaches are compared and the accuracy levels in diseases diagnosis are compared among three different data set. Among the three-algorithm considered decision tree algorithm shows a promising result.
Keywords: Machine-learning, Decision tree, IoT, Diagnosis schemes
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