Early Detection and Diagnosis of Chronic Kidney and Breast Cancer Using Multi-level Machine Learning: A Hybrid Prediction Model


  • Sun Hujun Malaysia University of Science and Technology, Petaling Jaya, Malaysia
  • Ang Ling Weay Malaysia University of Science and Technology, Petaling Jaya, Malaysia


Chronic kidney disease (CKD), Breast cancer, Multi-level machine learning, Hybrid prediction model, , Early detection and diagnosis


In this study, a multilevel machine learning approach is proposed for the early detection and diagnosis of chronic kidney disease (CKD) and breast cancer. The proposed hybrid prediction model uses a combination of supervised and unsupervised machine learning techniques, including Long Short-Term Memory (LSTM) and random forest algorithms, to improve the early detection and diagnosis of these diseases. The model also includes a feature selection process to extract the most relevant features from the data. The performance of the proposed model was evaluated on a dataset of patient information and compared with other machine learning models and traditional diagnostic methods. The results show that the proposed model outperforms traditional diagnostic methods and other machine learning models in terms of accuracy, sensitivity, and specificity in the early detection and diagnosis of CKD and breast cancer. The proposed multilevel machine learning approach provides an effective way to improve the early detection and diagnosis of CKD and breast cancer and has the potential to be used in clinical practice to improve patient outcomes.


Malakar, S., Roy, S. D., Das, S., Sen, S., Velásquez, J. D., & Sarkar, R. (2022). Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades. Archives of computational methods in engineering : state of the art reviews, 29(7), 5525–5567. https://doi.org/10.1007/s11831-022-09776-x

Uddin, S., Khan, A., Hossain, M. et al. Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 19, 281 (2019). https://doi.org/10.1186/s12911-019-1004-8

Charilaou, P., & Battat, R. (2022). Machine learning models and over-fitting considerations. World journal of gastroenterology, 28(5), 605–607. https://doi.org/10.3748/wjg.v28.i5.605

Alfred, R., & Obit, J. H. (2021). The roles of machine learning methods in limiting the spread of deadly diseases: A systematic review. Heliyon, 7(6), e07371. https://doi.org/10.1016/j.heliyon.2021.e07371

Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2022). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of ambient intelligence and humanized computing, 1–28. Advance online publication. https://doi.org/10.1007/s12652-021-03612-z

Bharati, S., Podder, P., & Mondal, M. R. H. (2020). Hybrid deep learning for detecting lung diseases from X-ray images. Informatics in Medicine Unlocked, 20, 100391.

Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017). Disease prediction by machine learning over big data from healthcare communities. IEEE Access, 5, 8869-8879.

Poonia, R. C., Gupta, M. K., Abunadi, I., Albraikan, A. A., Al-Wesabi, F. N., & Hamza, M. A. (2022). Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease. Healthcare.

Zhang, D., Zou, L., Zhou, X., & He, F. (2018). Integrating feature selection and feature extraction methods with deep learning to predict clinical outcome of breast cancer. IEEE Access, 6, 28936-28944.

Raghupathi, V., & Raghupathi, W. (2017). Preventive healthcare: A neural network analysis of behavioral habits and chronic diseases. Healthcare.

Malathi, D., Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., & Sangaiah, A. K. (2019). Hybrid reasoning-based privacy-aware disease prediction support system. Computers & Electrical Engineering, 73, 114-127.

Chen, G., Ding, C., Li, Y., Hu, X., Li, X., Ren, L., Ding, X., Tian, P., & Xue, W. (2020). Prediction of chronic kidney disease using adaptive hybridized deep convolutional neural network on the internet of medical things platform. IEEE Access, 8, 100497-100508.

Torrisi, M., Pollastri, G., & Le, Q. (2020). Deep learning methods in protein structure prediction. Computational and Structural Biotechnology Journal, 18, 1301-1310.

Walsh, S. L., Humphries, S. M., Wells, A. U., & Brown, K. K. (2020). Imaging research in fibrotic lung disease; applying deep learning to unsolved problems. The Lancet Respiratory Medicine, 8(11), 1144-1153.

Pournaghi, S. M., Bayat, M., & Farjami, Y. (2020). MedSBA: a novel and secure scheme to share medical data based on blockchain technology and attribute-based encryption. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4613-4641.




How to Cite

Sun Hujun, & Ang Ling Weay. (2023). Early Detection and Diagnosis of Chronic Kidney and Breast Cancer Using Multi-level Machine Learning: A Hybrid Prediction Model. International Journal of Sciences: Basic and Applied Research (IJSBAR), 67(1), 311–316. Retrieved from https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/15336