Developing a decision support system for osteoporosis Prediction

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

LANGARIZADEH MOSTAFA; Owji Leila; OROOJI AZAM

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF HEALTH ADMINISTRATION Year:2019 | Volume:21 | Issue:74 Page(s): 87-100

Download Full-Text

Persian Verion

View:

1,068

Download:

Cites:

Information Journal Paper

Title

Developing a decision support system for osteoporosis Prediction

Author(s)

LANGARIZADEH MOSTAFA | Owji Leila | OROOJI AZAM | Issue Writer Certificate

Keywords

OsteoporosisQ1

Clinical risk factorsQ1

artificial neural networkQ1

Abstract

Introduction: Osteoporosis is a common disease in women. Osteoporosis fractures may cause irreparable damages; therefore, early diagnosis and treatment before fractures is an important issue. The ojectiveof this study was to develop a decision support system for diagnosing Osteoporosis using artificial neural networks. Method: This developmental study has been done in second half of 2017 based on crossectional method. In present study initially Osteoporosis affecting clinical factors were identified and the most significant clinical factors were selected through incorporation of a questionnaire-based survey. Subsequently, information of 256 female participants and their BMD test results, five years after initial data entry were used to train neural network. The information was obtained fromwomen who refered to department of Bone Mineral Densityof oushehr university of medical sciences. In order to identify the best network, trial and error method was used and neural networks with different topologies were trained using Scaled Conjugate Gradient and Levenberg-Marquardt algorithms. Confusion matrix was used to evaluate the network’ s accuracy, sensitivity and specificity. Results: In the first stage, out of 15 essential variables, 12 variables were selected as the most important risk factors. Multilayer perceptron neural network was designed. Results showed that the best structure of network was due to Scaled Conjugate Gradient algorithm with 10 neurons and Levenberg-Marquardt algorithm with 12 neurons in hidden layer. Accuracy comparison was showed that generally Levenberg-Marquardt algorithm had better result. The best sensitivity, specificity, and accuracy was 83. 1%, 89. 4%, and 86. 3% respectively. Conclusion: In this study developed a diagnostic tool based on local data that could be effective in tracking Osteoporosis. Utilizing such a diagnostic tool as a timely referral of individuals and initiating therapy as soon as possible may prevent fractures from occurring and help avoiding the frequent complications of Osteoporosis.

Cites

No record.

References

Cite

APA: Copy

LANGARIZADEH, MOSTAFA, Owji, Leila, & OROOJI, AZAM. (2019). Developing a decision support system for osteoporosis Prediction. JOURNAL OF HEALTH ADMINISTRATION, 21(74 ), 87-100. SID. https://sid.ir/paper/129985/en

Vancouver: Copy

LANGARIZADEH MOSTAFA, Owji Leila, OROOJI AZAM. Developing a decision support system for osteoporosis Prediction. JOURNAL OF HEALTH ADMINISTRATION[Internet]. 2019;21(74 ):87-100. Available from: https://sid.ir/paper/129985/en

IEEE: Copy

MOSTAFA LANGARIZADEH, Leila Owji, and AZAM OROOJI, “Developing a decision support system for osteoporosis Prediction,” JOURNAL OF HEALTH ADMINISTRATION, vol. 21, no. 74 , pp. 87–100, 2019, [Online]. Available: https://sid.ir/paper/129985/en

Related Journal Papers