PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Unlabeled data is a significant problem in healthcare and other fields that deal with huge datasets. Unsupervised learning has the potential to be an effective solution in this case. The use of unsupervised algorithms in disease diagnosis has not been widely explored. In this work, we have developed a clustering algorithm to analyze the gliomas using Magnetic Resonance Imaging (MRI) data. Glioma is a severe medical illness that necessitates an accurate and timely diagnosis to establish effective treatment options. We used Pyradiomics to extract radiomic characteristics from MRI scans, which were then fed into a number of clustering methods, with cluster fitness assessed using primary assessment metrics. The best clustering algorithm was used as the pre-processor and to train major classification algorithms. In this study, we examined the performance of three prominent clustering algorithms, with agglomerative clustering outperforming the others. We achieved 0.83 Silhouette Coefficient, 0.21 Davies-Bouldin Index, and 323.22 Calinski-Harabasz Index values using aggregative clustering using Pyradiomics features. The decision tree strategy outperformed all classification methods, achieving 99.54% accuracy when clustering was applied to preprocess the data before classification. The proposed work has considerable potential for faster and more accurate analysis of medical image problems, especially in gliomas.

• Aik, L. E., Choon, T. W., & Abu, M. S. (2023). K-means algorithm based on flower pollination algorithm and Calinski-Harabasz Index. In Journal of Physics: Conference Series (Vol. 2643, No. 1, p. 012019). IOP Publishing. https://doi.org/10.1088/1742-6596/2643/1/012019
  
  Al‐Saeed, O., Ismail, M., Athyal, R. P., Rudwan, M., & Khafajee, S. (2009). T1‐weighted fluid‐attenuated inversion recovery and T1‐weighted fast spin‐echo contrast‐enhanced imaging: A comparison in 20 patients with brain lesions. Journal of Medical Imaging and Radiation Oncology, 53(4), 366-372. https://doi.org/10.1111/j.1754-9485.2009.02093.x
  
  Ashari, I. F., Nugroho, E. D., Baraku, R., Yanda, I. N., & Liwardana, R. (2023). Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index evaluation on K-means algorithm for classifying flood-affected areas in Jakarta. Journal of Applied Informatics and Computing, 7(1), 95-103. https://doi.org/10.30871/jaic.v7i1.4947
  
  Bhaskar, N., Bairagi, V., Boonchieng, E., & Munot, M. V. (2023). Automated detection of diabetes from exhaled human breath using deep hybrid architecture. IEEE Access, 11, 51712-51722. https://doi.org/10.1109/ACCESS.2023.3278278
  
  Bhaskar, N., Tupe-Waghmare, P., Nikam, S. S., & Khedkar, R. (2023). Computer-aided automated detection of kidney disease using supervised learning technique. International Journal of Electrical and Computer Engineering (IJECE), 13(5), 5932-5941. https://doi.org/10.11591/ijece.v13i5.pp5932-5941
  
  Bhattacharjee, S., Hwang, Y. B., Sumon, R. I., Rahman, H., Hyeon, D. W., Moon, D., Carole, K. S., Kim, H. C., & Choi, H. K. (2022). Cluster analysis: Unsupervised classification for identifying benign and malignant tumors on whole slide image of prostate cancer. In 2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/IPAS55744.2022.10052952
  
  Bibi, M., Abbasi, W. A., Aziz, W., Khalil, S., Uddin, M., Iwendi, C., & Gadekallu, T. R. (2022). A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis. Pattern Recognition Letters, 158, 80-86. https://doi.org/10.1016/j.patrec.2022.04.004
  
  Bougacha, A., Boughariou, J., Slima, M. B., Hamida, A. B., Mahfoudh, K. B., Kammoun, O., & Mhiri, C. (2018). Comparative study of supervised and unsupervised classification methods: Application to automatic MRI glioma brain tumors segmentation. In 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/ATSIP.2018.8364463
  
  Chen, R., Smith-Cohn, M., Cohen, A. L., & Colman, H. (2017). Glioma subclassifications and their clinical significance. Neurotherapeutics, 14, 284-297. https://doi.org/10.1007/s13311-017-0519-x
  
  Choudhary, S., Kumar, A., & Choudhary, S. (2022). Prediction and comparison of diabetes with logistic regression, Naïve Bayes, random forest, and support vector machine. In International Conference on Innovations in Computer Science and Engineering (pp. 273-283). Springer. https://doi.org/10.1007/978-981-19-7455-7_20
  
  Dike, H. U., Zhou, Y., Deveerasetty, K. K., & Wu, Q. (2018). Unsupervised learning based on artificial neural network: A review. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) (pp. 322-327). IEEE Publishing. https://doi.org/10.1109/CBS.2018.8612259
  
  Ganini, C., Amelio, I., Bertolo, R., Bove, P., Buonomo, O. C., Candi, E., Cipriani, C., Daniele, N. D., Juhl, H., Mauriello, A., Marani, C., Marshall, J., Melino, S., Marchetti, P., Montanaro, M., Natale, M. E., Novelli, F., Palmieri, G., Piacentini, M., ... & Melino, G. (2021). Global mapping of cancers: The cancer genome atlas and beyond. Molecular Oncology, 15(11), 2823-2840. https://doi.org/10.1002/1878-0261.13056
  
  Govender, P., & Sivakumar, V. (2020). Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmospheric Pollution Research, 11(1), 40-56. https://doi.org/10.1016/j.apr.2019.09.009
  
  Griffiths, A., Robinson, L. A., & Willett, P. (1984). Hierarchic agglomerative clustering methods for automatic document classification. Journal of Documentation, 40(3), 175-205. https://doi.org/10.1108/eb026764
  
  Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences, 374(2065), Article 20150202. https://doi.org/10.1098/rsta.2015.0202
  
  Madan, S., & Dana, K. J. (2016). Modified balanced iterative reducing and clustering using hierarchies (m-BIRCH) for visual clustering. Pattern Analysis and Applications, 19, 1023-1040. https://doi.org/10.1007/s10044-015-0472-4
  
  Mansour, R. F., Escorcia-Gutierrez, J., Gamarra, M., Gupta, D., Castillo, O., & Kumar, S. (2021). Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification. Pattern Recognition Letters, 151, 267-274. https://doi.org/10.1016/j.patrec.2021.08.018
  
  Naeem, S., Ali, A., Anam, S., & Ahmed, M. M. (2023). An unsupervised machine learning algorithm: Comprehensive review. International Journal of Computing and Digital Systems, 13(1), 911-921. http://dx.doi.org/10.12785/ijcds/130172
  
  Song, J., Gu, Y., & Kumar, E. (2023). Chest disease image classification based on spectral clustering algorithm. Research Reports on Computer Science, 2(1), 77-90. https://doi.org/10.37256/rrcs.2120232742
  
  Tupe-Waghmare, P., Malpure, P., Kotecha, K., Beniwal, M., Santosh, V., Saini, J., & Ingalhalikar, M. (2021). Comprehensive genomic subtyping of glioma using semi-supervised multi-task deep learning on multimodal MRI. IEEE Access, 9, 167900-167910. https://doi.org/10.1109/ACCESS.2021.3136293
  
  Van Griethuysen, J. J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillion-Robin, J. C., Pieper, S., & Aerts, H. J. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21), e104-e107. https://doi.org/10.1158/0008-5472.CAN-17-0339
  
  Wahyuningrum, T., Khomsah, S., Suyanto, S., Meliana, S., Yunanto, P. E., & Al Maki, W. F. (2021). Improving clustering method performance using K-means, mini batch K-means, BIRCH and spectral. In 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 206-210). IEEE Publishing. https://doi.org/10.1109/ISRITI54043.2021.9702823
  
  Zhang, L., Huang, D., Chen, X., Zhu, L., Xie, Z., Chen, X., Cui, G., Zhou, Y., Huang, G., & Shi, W. (2023). Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification. Journal of Biophotonics, 16(7), Article 202300020. https://doi.org/10.1002/jbio.202300020
  
  Zheng, L., Zhang, M., Hou, J., Gong, J., Nie, L., Chen, X., Zhou, Q., & Chen, N. (2020). High‐grade gliomas with isocitrate dehydrogenase wild‐type and 1p/19q codeleted: A typical molecular phenotype and current challenges in molecular diagnosis. Neuropathology, 40(6), 599-605. https://doi.org/10.1111/neup.12672