Email this article 
A Decision Support System Based on Neural Network and Genetic Algorithm: Case Study of Breast Cancer
Views
Abstract
Introduction: Artificial intelligence has been changing the way healthcare has been provided in many high-risk environments or areas with poor healthcare facilities. The emergence of epidemics and new diseases, as well as the crucial role of early diagnosis in prevention and the adoption of more effective treatments have highlighted the need for accurate design and self-organization of Clinical Decision Support Systems (CDSSs).
Material and Methods: In this study, a CDSS based on a neural networks (NN) and genetic algorithm is proposed. Since, on the one hand, the performance of the neural network (NN) is highly dependent on its parameters, and on the other hand, there is a constant need for optimization experts to fine-tune the parameters in the use of new data, a new chromosomal structure is proposed to automatically extract the optimal NN architecture, the number of layers and neurons. The goal is to increase the reusability of the model and ease of use by health experts.
Results: To evaluate the performance of the model, two standard breast cancer (BC) datasets, WBC and WDBC, were used. The model uses 70% of the data set for training and the remaining equally used for evaluation and testing. The test accuracy of the proposed model on WBC and WDBC datasets was 98.51 and 97.55%, respectively. The optimal NN architecture on WBC consisted a three-hidden layers with 18, 15 and 19 neurons in each layers, and on WDBC consisted one hidden layer with a single neuron.
Conclusion: The proposed chromosomal structure is able to derive optimal NN architecture. In according to the high classification accuracy of the model in the diagnosis of BC and providing the different architectures in accordance with the hardware implementation considerations, the proposed model can be used effectively in the diagnosis of other diseases.Keywords
References
Wang D, wan SH, Guizani N. Context-based probability neural network classifiers realized by genetic optimization for medical decision making. Multimedia Tools and Applications. 2018; 77(17): 21995-2006.
Seera M, Lim CP. A hybrid intelligent system for medical data classification. Expert Systems with Applications. 2014; 41(5): 2239-49.
Porebski S, Straszecka E. Extracting easily interpreted diagnostic rules. Information Sciences. 2018; 426: 19-37.
Efimenko M, Ignatev A, Koshechkin K. Review of medical image recognition technologies to detect melanomas using neural networks. BMC Bioinformatics. 2020; 21(11 Suppl): 270. PMID: 32921304 DOI: 10.1186/s12859-020-03615-1
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020; 395(10236): 1579-86. PMID: 32416782 DOI: 10.1016/S0140-6736(20)30226-9
Zarbakhsh P, Addeh A. Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network with optimal structure. J Cancer Res Ther. 2018; 14(3): 625-33. PMID: 29893330 DOI: 10.4103/0973-1482.183561
Uehara D, Hayashi Y, Seki Y, Kakizaki S, Horiguchi N, Tojima H, et al. Non-invasive prediction of non-alcoholic steatohepatitis in Japanese patients with morbid obesity by artificial intelligence using rule extraction technology. World J Hepatol. 2018; 10(12): 934-43. PMID: 30631398 DOI: 10.4254/wjh.v10.i12.934
Mittapalli PS, Thanikaiselvan V. Multiscale CNN with compound fusions for false positive reduction in lung nodule detection. Artif Intell Med. 2021; 113: 102017. PMID: 33685584 DOI: 10.1016/j.artmed.2021.102017
Boroumandzadeh M, Parvinnia E. Proposing a clinical decision support system for breast cancer diagnosis. Knowledge and Health in Basic Medical Sciences. 2020; 15(3): 54-66.
Akbarin MM, Shirdel A, Bari A, Mohaddes ST, Rafatpanah H, Karimani EG, et al. Evaluation of the role of TAX, HBZ, and HTLV-1 proviral load on the survival of ATLL patients. Blood Res. 2017; 52(2): 106-11. PMID: 28698846 DOI: 10.5045/br.2017.52.2.106
Ahouz F, Golabpour A. Predicting the incidence of COVID-19 using data mining. BMC Public Health. 2021; 21(1): 1-12. PMID: 34098928 DOI: 10.1186/s12889-021-11058-3
Yedjour D. Extracting classification rules from artificial neural network trained with discretized inputs. Neural Processing Letters. 2020; 52(3): 2469-91.
Abdel-Zaher AM, Eldeib AM. Breast cancer classification using deep belief networks. Expert Systems with Applications. 2016; 46: 139-44.
Karthik S, Srinivasa Perumal R, Chandra Mouli PVSSR. Breast cancer classification using deep neural networks. In: Anouncia SM, Wiil UK (eds). Knowledge computing and its applications: Knowledge manipulation and processing techniques. Vol 1. Springer; 2018.
Bakaeean B, Gholamin M, Tabatabaee Yazdi SA, Forghani MN. Clinical diagnostic value of cytokeratin 19 in breast cancer. Knowledge and Health in Basic Medical Sciences. 2020; 15(3): 2-10.
Rajathi GM. Optimized radial basis neural network for classification of breast cancer images. Curr Med Imaging. 2021; 17(1): 97-108. PMID: 32416697 DOI: 10.2174/1573405616666200516172118
World Health Organization. Latest global cancer data: Cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020 [Internet]. 2020 [cited: 15 Apr 2022]. Available from: https://www.iarc.who.int/wp-content/uploads/2020/12/pr292_E.pdf
Masjedi A, Hashemi V, Hojjat-Farsangi M, Ghalamfarsa G, Azizi G, Yousefi M, et al. The significant role of interleukin-6 and its signaling pathway in the immunopathogenesis and treatment of breast cancer. Biomed Pharmacother. 2018; 108: 1415-24. PMID: 30372844 DOI: 10.1016/j.biopha.2018.09.177
Tapak L, Shirmohammadi-Khorram N, Amini P, Alafchi B, Hamidi O, Poorolajal J. Prediction of survival and metastasis in breast cancer patients using machine learning classifiers. Clinical Epidemiology and Global Health. 2018; 7(3): 293-9.
Chaurasia V, Pal S, Tiwari B. Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology. 2018; 12(2): 119-26.
Gandomkar Z, Brennan PC, Mello-Thoms C. MuDeRN: Multi-category classification of breast histopathological image using deep residual networks. Artif Intell Med. 2018; 88: 14-24. PMID: 29705552 DOI: 10.1016/j.artmed.2018.04.005
Hayashi Y, Nakano S. Use of a Recursive-Rule eXtraction algorithm with J48graft to achieve highly accurate and concise rule extraction from a large breast cancer dataset. Informatics in Medicine Unlocked. 2015; 1: 9-16.
Breast Cancer Wisconsin (Original) Data Set [Internet]. 1992 [cited: 10 Agu 2022]. Available from: https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)
Prakash SS, Visakha K. Breast cancer malignancy prediction using deep learning neural networks. International Conference on Inventive Research in Computing Applications. IEEE; 2020.
Street WN, Wolberg WH, Mangasarian OL. Nuclear feature extraction for breast tumor diagnosis. International Symposium of Elaectronic Imaging. Society of Photo-optical Instrumentation Engineers; 1993.
Breast Cancer Wisconsin (Diagnostic) Data Set [Internet]. 1993 [cited: 10 Aug 2022]. Available from: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
Dehghan P, Mogharabi M, Zabbah I, Layeghi K, Maroosi A. Modeling breast cancer using data mining methods. Journal of Health and Biomedical Informatics. 2018; 4(4): 266-78.
Wiese M, Runge-Borchert G, Raatz A. Optimization of neural network hyperparameters for modeling of soft pneumatic actuators. In: Carbone G, Ceccarelli M, Pisla D (eds). New trends in medical and service robotics: Advances in theory and practice. MESROB; 2018.
Mikolajczyk A, Grochowski M, Kwasigroch A. Optimal selection of input features and an acompanying neural network structure for the classification purposes: Skin lesions case study. International Conference on Methods & Models in Automation & Robotics. IEEE; 2018.
Dheeba J, Albert Singh N, Tamil Selvi S. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J Biomed Inform. 2014; 49: 45-52. PMID: 24509074 DOI: 10.1016/j.jbi.2014.01.010
Pirmoradi S, Teshnehlab M, Zarghami N, Sharifi A. The self-organizing restricted Boltzmann machine for deep representation with the application on classification problems. Expert Systems with Applications. 2020; 149: 113286.
Lee S, Kim J, Kang H, Kang D, Park J. Genetic algorithm based deep learning neural network structure and hyperparameter optimization. Applied Sciences. 2021; 11(2): 744.
Koza JR, Rice JP. Genetic generation of both the weights and architecture for a neural network. International Joint Conference on Neural Networks. IEEE; 1991.
D'Ascenzo F, De Filippo O, Gallone G, Mittone G, Deriu MA, Iannaccone M, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): A modelling study of pooled datasets. Lancet. 2021; 397(10270): 199-207. PMID: 33453782 DOI: 10.1016/S0140-6736(20)32519-8
Vahabli E, Rahmati S. Improvement of FDM parts' surface quality using optimized neural networks: Medical case studies. Rapid Prototyping Journal. 2017; 23(4): 825-42.
Singh S, Singh S, Banga VK. Design of fuzzy logic system framework using evolutionary techniques. Soft Computing. 2020; 24: 4455-68.
Shi Y, Eberhart R, Chen Y. Implementation of evolutionary fuzzy systems. IEEE Transactions on Fuzzy Systems. 1999; 7(2): 109-19.
Gorzalczany MB, Rudzinski F. Interpretable and accurate medical data classification: A multi-objective genetic-fuzzy optimization approach. Expert Systems with Appllications. 2017; 71: 26-39.
Gowda Karegowda A, Jayaram MA, Manjunath AS, Vidya T, Shama. GA based dimension reduction for enhancing performance of k-means and fuzzy k-means: A case study for categorization of medical dataset. International Conference on Bio-Inspired Computing: Theories and Applications. Springer; 2013.
De Santis E, Rizzi A, Sadeghian A. Hierarchical genetic optimization of a fuzzy logic system for energy flows management in microgrids. Appllied Soft Computing. 2017; 60: 135-49.
Kootiani AZM, Doostari M, Golabpour A, Broujerdian M. Differential power analysis in the smart card by data simulation. International Conference on MultiMedia and Information Technology. IEEE; 2008.
Golabpour A, Shirazi HM, Farahi A, Kootiani AZM, Beigi H. A fuzzy solution based on Memetic algorithms for timetabling. International Conference on MultiMedia and Information Technology. IEEE; 2008.
Goudarzi M, Maghooli K. Extraction of fuzzy rules at different concept levels related to image features of mammography for diagnosis of breast cancer. Biocybernetics and Biomedical Engineering. 2018; 38(4): 1004-14.
DOI: https://doi.org/10.30699/fhi.v11i1.375
Refbacks
- There are currently no refbacks.
Frontiers in Health Informatics (Official Journal of Iranian Association of Medical Informatics) ISSN-Online: 2676-7104
No. 4, Rashid Yasami Av., Vali-e-Asr St., Tehran-Iran; SMS: (+98) 9383621232 email: info@ijmi.ir
This work is licensed under a Creative Commons Attribution 4.0 International License which allows users to read, copy, distribute and make derivative works from the material, as long as the author of the original work is cited properly.
The theme is developed by: Gostaresh Afzar Hamara