Osteoporosis Risk Predictive Model Using Supervised Machine Learning Algorithms
Volume 5, Issue 6, December 2017, Pages: 78-87
Received: Oct. 24, 2017;
Accepted: Nov. 9, 2017;
Published: Jan. 20, 2018
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Egejuru Ngozi Chidozie, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Mhambe Priscilla Dooshima, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Balogun Jeremiah Ademola, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Femi Komolafe, Engineering Materials Development Institute, Federal Ministry of Science & Technology, Akure, Nigeria
Idowu Peter Adebayo, Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
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In this paper, we developed a model to forecast the risk of osteoporosis using supervised machine learning algorithm. The study identified the variables that were monitored by experts in determining osteoporosis risk, formulated and simulated the predictive model. The performance of the model validation was also performed. This was with a view of developing a predictive model for the classification of osteoporosis risk among patients in Nigeria. A review of extensive literature surrounding the body of knowledge of osteoporosis risk revealed the associated risk factors used were identified and validated by experts, while historical data explaining the relationship between the risk factors and osteoporosis risk was collected. The predictive model for osteoporosis risk was formulated using two (2) supervised machine learning algorithms, namely Naïve Bayes’ (NB) classifier and the Multi-layer Perceptron (MLP) based on the identified risk factors. The results of the identification and data collection showed that there were 20 risk factors identified including the CD4 count level stratified as low, moderate and high risk based on information collected from 45 patients in Nigerian hospitals. The results of the model validation using the 10-fold cross validation revealed that the MLP had the best performance with a value of 100% over the accuracy of NB with a value of 71.4%. The result further showed that the performance of the MLP over the NB was influenced by the ability of the complex nature of the perceptron network to model the problem of identifying the risk of osteoporosis from the values of the risk factors presented in the training dataset. The study concluded that a better understanding of the relationship between the variables will improve the ability of the experts to determine the risk of osteoporosis during the examination of patients.
Osteoporosis Risk Classification, Predictive Modeling, Machine Learning
To cite this article
Egejuru Ngozi Chidozie,
Mhambe Priscilla Dooshima,
Balogun Jeremiah Ademola,
Idowu Peter Adebayo,
Osteoporosis Risk Predictive Model Using Supervised Machine Learning Algorithms, Science Research.
Vol. 5, No. 6,
2017, pp. 78-87.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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