Enhancing Ovarian Cancer Detection Using Self-Organizing Maps and Improved Recurrent Neural Networks with Extended Harmony Search Optimization
Priya Vij 1*, Patil Manisha Prashant 1
Journal of Angiotherapy 8(9) 1-5 https://doi.org/10.25163/angiotherapy.899877
Submitted: 03 July 2024 Revised: 30 August 2024 Published: 08 September 2024
Abstract
Background: Ovarian cancer (OC) is a highly fatal malignancy of the female reproductive system, characterized by its high mortality rate and the challenges associated with clinical research due to the disease's complexity and late-stage diagnosis. Advances in technology, such as the Internet of Medical Things (IoMT), offer new opportunities for improving OC detection and diagnosis. Objective: This study aimed to develop and evaluate a novel method for OC detection using IoMT data, leveraging Self-Organizing Maps (SOM) and Improved Recurrent Neural Networks (IRNN) enhanced with the Extended Harmony Search Optimization (EHSO) algorithm to improve feature selection and classification accuracy. Methods: The study utilized OC data from the IoMT and applied SOM for feature selection, which helps in managing and classifying large datasets. SOM was employed to improve data representation and address challenges in labeling and classifying data. The IRNN model, optimized using the EHSO algorithm, was developed to enhance classification performance. The model was tested using a dataset from Kaggle comprising 179 benign and 172 malignant OC images with 50 attributes. Results: The IRNN model with EHSO demonstrated superior performance compared to other methods. In the training dataset, it achieved an accuracy of 95.72% and a Root Mean Square Error (RMSE) of 4.8%. For the testing dataset, the model maintained a high accuracy of 90.4% and an RMSE of 6.8%. The IRNN with EHSO outperformed alternative methods in terms of specificity and sensitivity, while the Genetic Algorithm (GA) showed the lowest performance across all metrics. Conclusion: The proposed method using SOM and IRNN with EHSO significantly improves the detection of ovarian cancer by optimizing feature selection and classification accuracy. This approach offers a promising advancement in utilizing IoMT data for early and accurate OC diagnosis, potentially enhancing patient outcomes through more effective detection and treatment strategies.
Keywords: Ovarian Cancer, Internet of Medical Things, Self-Organizing Maps, Recurrent Neural Networks, Extended Harmony Search Optimization
References
Arfiani, A., & Rustam, Z. (2019, November). Ovarian cancer data classification using bagging and random forest. In AIP Conference Proceedings (Vol. 2168, No. 1). AIP Publishing.
Bae, J. H., Kim, M., Lim, J. S., & Geem, Z. W. (2021). Feature selection for colon cancer detection using k-means clustering and modified harmony search algorithm. Mathematics, 9(5), 570.
Chatterjee, P., Siddiqui, S., Granata, G., Dey, P., & Abdul Kareem, R. S. (2024). Performance analysis of five U-Nets on cervical cancer datasets. Indian Journal of Information Sources and Services, 14(1), 17–28.
Chen, S., Chen, Y., Yu, L., & Hu, X. (2021). Overexpression of SOCS4 inhibits proliferation and migration of cervical cancer cells by regulating JAK1/STAT3 signaling pathway. European Journal of Gynaecological Oncology, 42(3), 554-560.
Consiglio, A., Casalino, G., Castellano, G., Grillo, G., Perlino, E., Vessio, G., & Licciulli, F. (2021). Explaining ovarian cancer gene expression profiles with fuzzy rules and genetic algorithms. Electronics, 10(4), 375.
Cree, I. A., White, V. A., Indave, B. I., & Lokuhetty, D. (2020). Revising the WHO classification: female genital tract tumours. Histopathology, 76(1), 151-156.
Elhoseny, M., Bian, G. B., Lakshmanaprabu, S. K., Shankar, K., Singh, A. K., & Wu, W. (2019). Effective features to classify ovarian cancer data in internet of medical things. Computer Networks, 159, 147-156.
Engqvist, H., Parris, T. Z., Biermann, J., Rönnerman, E. W., Larsson, P., Sundfeldt, K., & Helou, K. (2020). Integrative genomics approach identifies molecular features associated with early-stage ovarian carcinoma histotypes. Scientific Reports, 10(1), 7946.
Escorcia-Gutierrez, J., Torrents-Barrena, J., Gamarra, M., Madera, N., Romero-Aroca, P., Valls, A., & Puig, D. (2022). A feature selection strategy to optimize retinal vasculature segmentation. Computers, Materials and Continua, 70(2), 1-15.
Ganguly, S. (2020). Multi-objective distributed generation penetration planning with load model using particle swarm optimization. Decision Making: Applications in Management and Engineering, 3(1), 30-42.
Hinchcliff, E., Westin, S. N., & Herzog, T. J. (2022). State of the science: Contemporary front-line treatment of advanced ovarian cancer. Gynecologic Oncology, 166(1), 18-24.
Paik, E. S., Lee, J. W., Park, J. Y., Kim, J. H., Kim, M., Kim, T. J., ... & Seo, S. W. (2019). Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. Journal of Gynecologic Oncology, 30(4).
Prat, J., D'Angelo, E., & Espinosa, I. (2018). Ovarian carcinomas: at least five different diseases with distinct histological features and molecular genetics. Human Pathology, 80, 11-27.
Ramakrishnan, J., Ravi Sankar, G., & Thavamani, K. (2019). Publication growth and research in India on lung cancer literature: A bibliometric study. Indian Journal of Information Sources and Services, 9(S1), 44-47.
Ramana, R. H. V., & Ravisankar, V. (2024). Precision in prostate cancer diagnosis: A comprehensive study on neural networks. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 15(2), 109-122. https://doi.org/10.58346/JOWUA.2024.I2.008
Shahane, S. (2024). Predict ovarian cancer [Dataset]. Kaggle. https://www.kaggle.com/saurabhshahane/predict-ovarian-cancer
Su, Y. N., Wang, M. J., Yang, J. P., Wu, X. L., Xia, M., Bao, M. H., & Fu, L. J. (2023). Effects of Yulin Tong Bu formula on modulating gut microbiota and fecal metabolite interactions in mice with polycystic ovary syndrome. Frontiers in Endocrinology, 14, 1122709.
Takahashi, A., Hong, L., & Chefetz, I. (2020). How to win the ovarian cancer stem cell battle: Destroying the roots. Cancer Drug Resistance, 3(4), 1021.
Yang, J. P., Ullah, A., Su, Y. N., Otoo, A., Adu-Gyamfi, E. A., Feng, Q., & Ding, Y. B. (2023). Glycyrrhizin ameliorates impaired glucose metabolism and ovarian dysfunction in a polycystic ovary syndrome mouse model. Biology of Reproduction, 109(1), 83-96.
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