Abstract
The process of identifying a disease that a patient is affected by with the help of signs, test reports, and symptoms is known as diagnosis. Deep learning plays a major role in automated diagnosis in the medical field. The efficiency of the automated diagnosis system depends on how well the data provided for training is and how it is used to train the system. The data is subject to data quality concerns like its accuracy, completeness, consistency, and data balance. Additionally, significantly, in reality, clinical data is created solely from many useful and important attributes, rather than the complete patient data. But, in the real world, data is of poor quality due to various reasons, for example, data validity, respectability, fulfillment, exactness, and so on. Specifically, in the medical domain also, the data is imbalanced and incomplete. So, in this project, we propose a multi-instance neural network to predict the disease based on the patients’ existing and reliable data. The proposed approach is planned to be tested with the imbalanced dataset named the Western Medicine (WM) and Disease Symptom Prediction. The proposed multi-instance neural network architecture predicts the disease with high accuracy.
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References
Ghahramani, Z., Jordan, M.I.: Supervised learning from incomplete data via EM approach. Adv. Neural Inf. Process. Syst. (2016)
Fung, G., et al.: Multiple instances learning for computer aided diagnosis. Adv. Neural Inf. Process. Syst. 19 (2007): 425
Belarouci, S., Chikh, M.A.: Medical imbalanced data classification. Adv. Sci. Technol. Eng. Syst. J. (2017)
Mehrabani-Zeinabad, K., et al.: An efficient and effective model to handle missing data in classification. BioMed Res. Int. (2020)
Lin, W.-C., et al.: Clustering-based under sampling in class-imbalanced data. Inf. Sci. 409:17–26 (2017)
D’Addabbo, A., Maglietta, R.: Parallel selective sampling method for imbalanced and large data classification. Pattern Recogn. Lett. 62, 61–67 (2015)
Wang, S., Yao, X.: Diversity analysis on imbalanced data sets by using ensemble models. In: 2009 IEEE Symposium on Computational Intelligence and Data Mining. IEEE (2009)
Yan, Y., et al.: Deep multi-instance learning with dynamic pooling. In: Asian Conference on Machine Learning. PMLR (2018)
Zeyuan, W., et al.: Attention-Based multi-instance neural network for medical diagnosis from incomplete and low-quality data
Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. arXiv preprint arXiv:1802.04712 (2018)
Khalilia, M., et al.: Predicting disease risks from highly imbalanced data using random forest. 29 July 2011
Fotouhi, S., et al.: A comprehensive data level analysis for cancer diagnosis on imbalanced data. J. Biomed. Inf. 90 (2019)
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Sribhashyam, S., Koganti, S., Vineela, M.V., Kalyani, G. (2022). Medical Diagnosis for Incomplete and Imbalanced Data. In: Satapathy, S.C., Peer, P., Tang, J., Bhateja, V., Ghosh, A. (eds) Intelligent Data Engineering and Analytics. Smart Innovation, Systems and Technologies, vol 266. Springer, Singapore. https://doi.org/10.1007/978-981-16-6624-7_49
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DOI: https://doi.org/10.1007/978-981-16-6624-7_49
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