ORIGINAL ARTICLE
A proactive model to predict osteoporosis: An artificial immune system approach
Keerthika Periasamy,
Suresh Periasamy,
Sathiyamoorthi Velayutham,
Zuopeng Zhang,
Syed Thouheed Ahmed,
Anitha Jayapalan,
Keerthika Periasamy
Department of CSE, Kongu Engineering College, Perundurai, Erode, India
Search for more papers by this authorSuresh Periasamy
Department of IT, Kongu Engineering College, Perundurai, Erode, India
Search for more papers by this authorSathiyamoorthi Velayutham
Department of CSE, Sona College of Technology, Salem, India
Search for more papers by this authorCorresponding Author
Zuopeng Zhang
Coggin College of Business, University of North Florida, Jacksonville, Florida, USA
Correspondence
Zuopeng Zhang, Coggin College of Business, University of North Florida, Jacksonville, FL 32224, USA.
Email: [email protected]
Search for more papers by this authorSyed Thouheed Ahmed
School of Computing and Information Technology, REVA University, Yelahanka, Bengaluru, India
Search for more papers by this authorAnitha Jayapalan
Department of CSE, RV Institute of Technology and Management, Bangalore, India
Search for more papers by this authorKeerthika Periasamy,
Suresh Periasamy,
Sathiyamoorthi Velayutham,
Zuopeng Zhang,
Syed Thouheed Ahmed,
Anitha Jayapalan,
Keerthika Periasamy
Department of CSE, Kongu Engineering College, Perundurai, Erode, India
Search for more papers by this authorSuresh Periasamy
Department of IT, Kongu Engineering College, Perundurai, Erode, India
Search for more papers by this authorSathiyamoorthi Velayutham
Department of CSE, Sona College of Technology, Salem, India
Search for more papers by this authorCorresponding Author
Zuopeng Zhang
Coggin College of Business, University of North Florida, Jacksonville, Florida, USA
Correspondence
Zuopeng Zhang, Coggin College of Business, University of North Florida, Jacksonville, FL 32224, USA.
Email: [email protected]
Search for more papers by this authorSyed Thouheed Ahmed
School of Computing and Information Technology, REVA University, Yelahanka, Bengaluru, India
Search for more papers by this authorAnitha Jayapalan
Department of CSE, RV Institute of Technology and Management, Bangalore, India
Search for more papers by this authorAbstract
Osteoporosis disease is caused by hormonal changes, vitamin D, and calcium deficiency. With current technologies, the identification of osteoporosis requires many tests with the support of medications. Bone mineral density is a typical measure implemented using a DEXA scan which can be very costly. Such high technology equipment is usually not accessible for remote people, and thus a low-cost screening system is very appealing. This article proposes an osteoporosis prediction system that effectively determines its possibility of occurrence based on essential factors such as smoking habits and calcium level so that the people at high risk can be referred to access the DEXA scanner. Our proposed system is implemented by an improved version of the artificial immune system, enabling care providers to take precautionary measures at the right time to avoid the early development of osteoporosis. The experiments demonstrated a promising result of 94% prediction accuracy that proved its usefulness in identifying people with potential osteoporosis in the future.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
REFERENCES
- Abdel-Basset, M., Chang, V., & Mohamed, R. (2020). A hybrid novel slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images. Applied Soft Computing, 95, 106642. https://doi.org/10.1016/j.asoc.2020.106642
- Abdel-Basset, M., Chang, V., Hawasha, H., Chakrabortty, R. K., & Ryan, M. (2021). FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection. Knowledge-Based Systems, 212, 106647.
- Alatas, B., & Akin, E. (2005). Mining fuzzy classification rules using an artificial immune system with boosting. In J. Eder, H. M. Haav, A. Kalja, & J. Penjam (Eds.), Advances in Databases and Information Systems (Vol. 3631, pp. 283–293). Springer.
10.1007/11547686_21 Google Scholar
- Biswas, S. K., Devi, D., & Chakraborty, M. (2018). A hybrid case based reasoning model for classification in internet of things (IoT) environment. Journal of Organizational and End User Computing (JOEUC), 30(4), 104–122. https://doi.org/10.4018/JOEUC.2018100107
- Carter, J. H. (2000). The immune system as a model for pattern recognition and classification. Journal of the American Medical Informatics Association, 7(1), 28–41.
- Chang, H. W., Chiu, Y. H., Kao, H. Y., Yang, C. H., & Ho, W. H. (2013). Comparison of classification algorithms with wrapper-based feature selection for predicting osteoporosis outcome based on genetic factors in a taiwanese women population. International Journal of Endocrinology. 2013, 1–8. https://doi.org/10.1155/2013/850735
- Chang, P. C., Lin, C. H., & Chen, M. H. (2016). A hybrid course recommendation system by integrating collaborative filtering and artificial immune systems. Algorithms, 9(3), 47–55. https://doi.org/10.3390/a9030047
- Chang, V. (2018a). Computational intelligence for medical imaging simulations. Journal of Medical Systems, 42(1), 10.
- Chang, V. (2018b). Data analytics and visualization for inspecting cancers and genes. Multimedia Tools and Applications, 77(14), 17693–17707.
- Chiu, J. S., Li, Y. C., Yu, F. C., & Wang, Y.-F. (2006). Applying an artificial neural network to predict osteoporosis in the elderly. Studies in Health Technology and Informatics, 124, 609–614.
- Couronné, R., Probst, P., & Boulesteix, A. L. (2018). Random forest versus logistic regression: A large-scale benchmark experiment. BMC Bioinformatics, 19(1), 270. https://doi.org/10.1186/s12859-018-2264-5
- Cruz, A. S., Lins, H. C., Medeiros, R., Filho, J., & da Silva, S. G. (2018). Artificial intelligence on the identification of risk groups for osteoporosis, a general review. Biomedical Engineering Online, 17(1), 12. https://doi.org/10.1186/s12938-018-0436-1
- Darmoul, S., Pierreval, H., & Gabouj, S. H. (2006). Scheduling using artificial immune system metaphors: A review. Paper presented at the Proceedings of the IEEE Conference on Service Systems and Service Management, France (pp. 1150–1155).
- Dasgupta, D. (1998). Artificial immune systems and their applications ( 1st ed.). Springer.
- Dasgupta, D., Ji, Z., & Gonzalez, F. (2003). Artificial immune systems research in the last five years. Paper presented at the Proceedings of the Congress on Evolutionary Computation Conference, Canberra, Australia (pp. 8–12).
- De Cos Juez, F. J., Suarez-Suarez, M. A., Lasheras, F. S., & Murcia-Mazon, A. (2011). Application of neural networks to the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Mathematical and Computer Modelling, 54(7), 1665–1670. https://doi.org/10.1016/j.mcm.2010.11.069
10.1016/j.mcm.2010.11.069 Google Scholar
- De Moura Meneses, A. A., Pinheiro, C. J. G., Schirru, R., Barroso, R. C., Braz, D., & Oliveira, L. F.. (2008). Artificial neural networks applied to bone recognition in X-ray computer microtomography imaging for histomorphometric analysis. Paper presented at the Nuclear Science Symposium Conference Record, Dresden,Germany, pp. 5309–5313. https://doi.org/10.1109/NSSMIC.2008.4774432
- De Moura Meneses, A. A., Pinheiro, C. J. G., Gambardella, L. M., Schirru, R., Barroso, R. C., Braz, D., & Oliveira, L. F. (2009). Neural computing for quantitative analysis of human bone trabecular structures in synchrotron radiation X-ray μCT images. Paper presented at the Nuclear Science Symposium Conference Record (NSS/MIC), Orlando,FL, USA. (pp. 3437–3441). https://doi.org/10.1109/NSSMIC.2009.5401781
- Dudek, G. (2012). An artificial immune system for classification with local feature selection. IEEE Transactions on Evolutionary Computation, 16, 847–860.
- Er, O., Cetin, O., Bascil, M. S., & Temurtas, F. (2016). A comparative study on Parkinson's disease diagnosis using neural networks and artificial immune system. Journal of Medical Imaging and Health Informatics, 6(1), 264–268. https://doi.org/10.1166/jmihi.2016.1606
- Harrar, K., Hamami, L., Akkoul, S., Lespessailles, E., & Jennane, R. (2012). Osteoporosis assessment using multilayer perceptron neural networks. Paper presented at the 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), Istanbul,Turkey, (pp. 217–221). https://doi.org/10.1109/IPTA.2012.6469528
- Holi, M. S., & Radhakrishnan, S. (2003). In vivo assessment of osteoporosis in women by impulse response technique. Paper presented at the Proceedings of the TENCON Conference on Convergent Technologies for Asia-Pacific Region, Bangalore,India, (pp. 1395–1398).
- Iliou, T., Anagnostopoulos, C. N., Stephanakis, I. M., & Anastassopoulos, G. (2017). A novel data preprocessing method for boosting neural network performance: A case study in osteoporosis prediction. Information Sciences, 380(Supplement C), 92–100. https://doi.org/10.1016/j.ins.2015.10.026
10.1016/j.ins.2015.10.026 Google Scholar
- Jennane, R., Almhdie-Imjabber, A., Hambli, R., Ucan, O. N., & Benhamou, C. L. (2010). Genetic algorithm and image processing for osteoporosis diagnosis. Paper presented at the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, BuenosAires, Argentina, (pp. 5597–5600).
- Jerne, N. K. (1974). Towards a network theory of the immune system. Annales d'Immunologie, 125C, 373–389.
- Kanis, J. A. (1994). Assessment of fracture risk and its application to screening for post-menopausal osteoporosis: Synopsis of a WHO report. WHO Study Group. Osteoporosis International, 4, 368–381.
- Kanis, J. A., Johnell, O., Oden, A., Jonsson, B., De Laet, C., & Dawson, A. (2000). Prediction of fracture from low BoneMineral density measurements overestimates risk. Bone, 26, 387–391.
- Kanis, J. A. on behalf of the World Health Organization Scientific Group. (2018). Assessment of osteoporosis at the primary health care level (Technical report). WHO Collaborating Centre, University of Sheffield.
- Kastner, M., Perrier, L., Munce, S. E. P., Adhihetty, C. C., Lau, A., Hamid, J., Treister, V., Chan, J., Lai, Y., & Straus, S. E. (2018). Complex interventions can increase osteoporosis investigations and treatment: A systematicreview and meta-analysis. Osteoporosis International, 29, 5–17.
- Kavitha, M. S., Asano, A., Taguchi, A., & Heo, M. S. (2013). The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis. Imaging Science in Dentistry, 43, 153–161.
- Koh, L. K., Sedrine, W. B., Torralba, T. P., Kung, A., Fujiwara, S., Chan, S. P., Huang, Q. R., Rajatanavin, R., Tsai, K. S., Park, H. M., & Reginster, J. Y. (2001). A simple tool to identify asian women at increased risk of osteoporosis. Osteoporosis International, 12, 699–705.
- Kung, A. W. C., Ho, A. Y. Y., Sedrine, W. B., Reginster, J. Y., & Ross, P. D. (2003). Comparison of a simple clinical risk index and quantitative bone ultrasound for identifying women at increased risk of osteoporosis. Osteoporosis International, 14(9), 716–721. https://doi.org/10.1007/s00198-003-1428-x
- Lee, S., Lee, J. W., Jeong, J. W., Yoo, D. S., & Kim, S. (2008). A preliminary study on discrimination of osteoporotic fractured group from nonfractured group using support vector machine. Paper presented at the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver, Canada, (pp. 474-477) https://doi.org/10.1109/IEMBS.2008.4649193
- Lemineur, G., Harba, R., Kilic, N., Ucan, O. N., Osman, O., & Benhamou, L.. (2007). Efficient estimation of osteoporosis using artificial neural networks. Paper presented at the 33rd Annual Conference of the IEEE 2007 Industrial Electronics Society, 2007. IECON 2007, Taipei,Taiwan, (pp. 3039–3044). https://doi.org/10.1109/IECON.2007.4460070
- Leslie, W. D., Lix, L. M., & Yogendran, M. S. (2011). Validation of a case definition for osteoporosis disease surveillance. Osteoporosis International, 22(1), 37–46. https://doi.org/10.1007/s00198-010-1225-2
- Liu, Q., Cui, X., Chou, Y. C., Abbod, M. F., Lin, J., & Shieh, J. S. (2015). Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders. Biomedical Signal Processing and Control, 21(4), 146–156. https://doi.org/10.1016/j.bspc.2015.06.002
10.1016/j.bspc.2015.06.002 Google Scholar
- Mao, S. S., Li, D., Luo, Y., Syed, Y. S., & Budoff, M. J. (2016). Application of quantitative computed tomography for assessment of trabecular bone mineral density, microarchitecture and mechanical property. Clinical Imaging, 40(2), 330–338. https://doi.org/10.1016/j.clinimag.2015.09.016
- Mantzaris, D., Anastassopoulos, G., Iliadis, L., Kazakos, K., & Papadopoulos, H.. (2010). Medical informatics and biomedical engineering. Paper presented at the Proceedings of the 6th IFIP WG 12.5 International Conference, AIAI 2010, Larnaca, Cyprus (pp. 120–127). Springer. https://doi.org/10.1007/978-3-642-16239-8
- Montechiesi, L., Cocconcelli, M., & Rubini, R. (2016). Artificial immune system via Euclidean distance minimization for anomaly detection in bearings. Mechanical Systems and Signal Processing, 76–77, 380–393. https://doi.org/10.1016/j.ymssp.2015.04.017
- Perelson, A. S., & Weisbuch, G. (1997). Immunology for physicists. Reviews of Modern Physics, 69(4), 1219.
- Prilianti, K. R., Callista, P. B., & Setiawan, H. (2017). Artificial immune system for diabetes meal plans optimization. AIP Conference Proceedings, 1825, 020017. https://doi.org/10.1063/1.4978986
10.1063/1.4978986 Google Scholar
- Reid, D. M., Mackay, I., Wilkinson, S., Miller, C., Schuette, D. G., Compston, J., Cooper, C., Duncan, E., Galwey, N., Keen, R., Langdahl, B., McLellan, A., Pols, H., Uitterlinden, A., O'Riordan, J., Wass, J. A. H., Ralston, S. H., & Bennett, S. T. (2006). Cross-calibration of dual-energy X-ray densitometers for a large, multi-center genetic study of osteoporosis. Osteoporosis International, 17(1), 125–132. https://doi.org/10.1007/s00198-005-1936-y
- Rizzi, A., Panella, M., Paschero, M., & Mascioli, F. F. (2004). Estimation of bone mineral density data using mog neural networks. IEEE International Joint Conference on Neural Networks., 4(1), 3241–3246.
- Roy, V., & Shukla, S. (2017). A methodical healthcare model to eliminate motion artifacts from big EEG data. Journal of Organizational and End User Computing (JOEUC), 29(4), 84–102. https://doi.org/10.4018/JOEUC.2017100105
- Sapthagirivasan, V., & Anburajan, M. (2013). Diagnosis of osteoporosis by extraction of trabecular features from hip radiographs using support vector machine: An investigation panorama with DXA. Computers in Biology and Medicine, 43(11), 1910–1919.
- Saurabh, P., & Verma, B. (2016). An efficient proactive artificial immune system based anomaly detection and prevention system. Expert Systems with Applications, 60, 311–320. https://doi.org/10.1016/j.eswa.2016.03.042
- Scanlan, J., Li, F. F., Umnova, O., Rakoczy, G., & Lövey, N. (2018). Machine learning and DSP algorithms for screening of possible osteoporosis using electronic stethoscopes. Paper presented at the Proceedings of the 3rd International Conference on Biomedical Imaging, Signal Processing. ACM,Bari Italy,(pp. 93–100).https://doi.org/10.1145/3288200.3288215.
- Sexton, R. S., Dorsey, R. E., & Johnson, J. D. (1999). Beyond backpropagation: Using simulated annealing for training neural networks. Journal of Organizational and End User Computing (JOEUC), 11(3), 3–10. https://doi.org/10.4018/joeuc.1999070101
10.4018/joeuc.1999070101 Google Scholar
- Sharif, M., Attique, M., Tahir, M. Z., Yasmim, M., Saba, T., & Tanik, U. J. (2020). A machine learning method with threshold based parallel feature fusion and feature selection for automated gait recognition. Journal of Organizational and End User Computing (JOEUC), 32(2), 67–92. https://doi.org/10.4018/JOEUC.2020040104
- Sung, S., Lee, P., Hsieh, C., & Zheng, W. (2020). Medication use and the risk of newly diagnosed diabetes in patients with epilepsy: A data mining application on a healthcare database. Journal of Organizational and End User Computing (JOEUC), 32(2), 93–108. https://doi.org/10.4018/JOEUC.2020040105
- Tafraouti, A., Hassouni, M. E., Toumi, H., Lespessailles, E., & Jennane, R. (2014). Osteoporosis diagnosis using fractal analysis and support vector machine. Paper presented at the 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, Marrakech,Morocco , (pp. 73–77). https://doi.org/10.1109/SITIS.2014.49
- Tejaswini, E., Vaishnavi, P., & Sunitha, R. (2016). Detection and prediction of osteoporosis using impulse response technique and artificial neural network. Paper presented at the Proceedings of the IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, (pp. 1571–1575).
- Wang, W., Richards, G., & Rea, S. (2005). Hybrid data mining ensemble for predicting osteoporosis risk. Paper presented at the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, (pp. 886–889). https://doi.org/10.1109/IEMBS.2005.1616557
- Xueni, Q., & Lau, H. Y. K. (2010). An AIS-based hybrid algorithm with PSO for job shop scheduling problem. IFAC Proceedings Volumes, 43(4), 350–355.
10.3182/20100701-2-PT-4011.00060 Google Scholar
- Yoo, T. K., Kim, S. K., Kim, D. W., Choi, J. Y., Lee, W. H., & Park, E. C. (2013). Osteoporosis risk prediction for bone mineral density assessment of post-menopausal women using machine learning. Yonsei Medical Journal, 54, 1321–1330.
- Yu, X., Ye, C., & Xiang, L. (2016). Application of artificial neural network in the diagnostic system of osteoporosis. Neurocomputing, 214(C), 376–381. https://doi.org/10.1016/j.neucom.2016.06.023
10.1016/j.neucom.2016.06.023 Google Scholar
