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Article:Intelligent Predictive Osteoporosis System

by Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 32 - Number 5
Year of Publication: 2011
Authors: Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB
10.5120/3901-5468

Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB . Article:Intelligent Predictive Osteoporosis System. International Journal of Computer Applications. 32, 5 ( October 2011), 28-37. DOI=10.5120/3901-5468

@article{ 10.5120/3901-5468,
author = { Walid MOUDANI, Ahmad SHAHIN, Fadi CHAKIK, Dima RAJAB },
title = { Article:Intelligent Predictive Osteoporosis System },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 32 },
number = { 5 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume32/number5/3901-5468/ },
doi = { 10.5120/3901-5468 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:24.100073+05:30
%A Walid MOUDANI
%A Ahmad SHAHIN
%A Fadi CHAKIK
%A Dima RAJAB
%T Article:Intelligent Predictive Osteoporosis System
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 5
%P 28-37
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The healthcare environment is generally perceived as being information rich yet knowledge poor. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. The information technology may provide alternative approaches to Osteoporosis disease diagnosis. In this study, we examine the potential use of classification techniques on a massive volume of healthcare data, particularly in prediction of patients that may have Osteoporosis Disease (OD) through its risk factors. For this purpose, we propose to develop a new solution approach based on Random Forest (RF) decision tree to identify the osteoporosis cases. There has been no research in using the afore-mentioned algorithm for Osteoporosis patients’ prediction. The reduction of the attributes consists to enumerate dynamically the optimal subsets of the reduced attributes of high interest by reducing the degree of complexity. A computer-aided system is developed for this purpose. The study population consisted of 2845 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

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Index Terms

Computer Science
Information Sciences

Keywords

Osteoporosis Disease Multi-Classifier Decision Trees Prediction features reduction

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