MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION

Authors:

Bhaskar Adepu,Dr. T. Archana,

DOI NO:

https://doi.org/10.26782/jmcms.2025.03.00009

Keywords:

Convolutional Deep Learning,Healthcare,Prediction,Stacking Ensemble Learning,Tuna Swarm Optimization,

Abstract

In the modern era of computers, numerous disciplines are witnessing the development of massive data volumes. Statistics are important in healthcare engineering because they provide insights into various diseases and match patient data. These datasets serve two functions: improving illness prediction and examining large data reservoirs to identify previously unknown disease patterns. Leveraging deep learning models, it becomes feasible to detect and forecast the early stages of numerous diseases based on individual health conditions. Nonetheless, the current landscape of illness prediction encounters several challenges such as inadequate large-scale datasets, logistical delays, the imperative for more precise and dependable predictions, and the intricacy of the models themselves. This paper introduces an innovative method for disease prediction utilizing deep learning, particularly focusing on an ensemble-based multi-disease prediction model. Datasets for lung cancer, cervical cancer, chronic renal disease, Parkinson's illness, and HCC survival are sourced from the trustworthy UCI repository for experimentation. A robust stacked deep ensemble model is proposed combining the InceptionResNetV2, EfficientNetV2L, and Xception architectures. This model integrates pre-processing techniques and employs the Tuna Swarm Optimization (TSO) Algorithm for feature selection in executing multi-label disease prediction. The suggested deep learning algorithms' performance is evaluated using criteria such as precision, specificity, sensitivity, accuracy, and error rate. This assessment demonstrates the potential of the recommended method to significantly contribute to the healthcare system by offering consistent and reliable predictions across various types of illnesses, as shown in a comparative analysis against existing models.

Refference:

I. Ampavathi, Anusha and VijayaSaradhi, T., : ‘Multi disease-prediction framework using hybrid deep learning: an optimal prediction model.’ Computer Methods in Biomechanics and Biomedical Engineering. Vol. 24(10), pp. 1146-1168, 2021. 10.1080/10255842.2020.1869726
II. Anand, Vatsala, et al., : ‘Weighted average ensemble deep learning model for stratification of brain tumor in MRI images.’ Diagnostics. Vol. 13(7), pp. 1320, 2023. 10.3390/diagnostics13071320
III. Deb, Sagar Deep, et al., : ‘A multi model ensemble based deep convolution neural network structure for detection of COVID19.’ Biomedical signal processing and control. Vol. 71, pp. 103126, 2022. 10.1016/j.bspc.2021.103126
IV. Dubey, A.K., : ‘Optimized hybrid learning for multi disease prediction enabled by lion with butterfly optimization algorithm.’ Sādhanā. Vol. 46(2), pp. 63, 2021. 10.1007/s12046-021-01574-8
V. Dwight L Evans, Dennis S Charney, : ‘A journal of psychiatric neuroscienceand therapeutics “Mood disorders and medical illness: a major public health problem.’ A journal of psychiatric neuroscience therapeutics. Vol. 54(13), pp. 177-180, 2003. 10.1016/S0006-3223(03)00639-5.
VI. Ehab, E., Xianghua, X., : ‘An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeat Arrhythmia classification.’ IEEE. Vol. 9, 2021. 10.1109/ACCESS.2021.3098986
VII. Essa, Ehab, and XianghuaXie, : ‘An ensemble of deep learning-based multi-model for ECG heartbeats arrhythmia classification.’ IEEE access, Vol. 9, pp. 103452-103464, 2021. 10.1109/ACCESS.2021.3098986
VIII. Hsu, C.H., Chen, X., Lin, W., Jiang, C., Zhang, Y., Hao, Z & Chung, Y.C.,: ‘Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning.’ Measurement. Vol. 175, pp. 109145, 2021. 10.1016/j.measurement.2021.109145

IX. https://archive.ics.uci.edu/dataset/174/parkinsons
X. https://archive.ics.uci.edu/dataset/336/chronic+kidney+disease
XI. https://archive.ics.uci.edu/dataset/383/cervical+cancer+risk+factors
XII. https://archive.ics.uci.edu/dataset/423/hcc+survival
XIII. https://archive.ics.uci.edu/dataset/62/lung+cancer
XIV. Ismail, Walaa, N., FathimathulRajeena, P.P and Mona, Ali, A.S., : ‘A meta-heuristic multi-objective optimization method for alzheimer’s disease detection based on multi-modal data.’ Mathematics. Vol. 11(4), pp. 957, 2023. 10.3390/math11040957
XV. Kevin Zhou, S., Hayit Greenspan, Christos Davatzikos, James Duncan, S., Bram Van Ginnek, : ‘A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends.’ Case Studies With Progress Highlights, and Future Promises. Vol. 109, pp. 5, 2021. 10.1109/JPROC.2021.3054390
XVI. Khamparia, Aditya, et al., : ‘A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders.’ Neural computing and applications. Vol. 32, pp. 11083-11095, 2020. 10.1007/s00521-018-3896-0
XVII. KhazaeeFadafen, Masoud, and KhosroRezaee, : ‘Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework.’ Scientific Reports. Vol. 13(1), pp. 8823, 2023. 10.1038/s41598-023-35431-x
XVIII. Liu, Hong, et al., : ‘Multi-model ensemble learning architecture based on 3D CNN for lung nodule malignancy suspiciousness classification.’ Journal of Digital Imaging. Vol. 33, pp. 1242-1256, 2020. 10.1109/ACCESS.2021.3098986
XIX. Saurabh, A., Arya, K.V., Meena, Y.K., : ‘MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification.’ 10.48550/arXiv.2401.00728
XX. Shaveta, D., Kumar, M., Ayyagari, M.R., Kumar, G., : ‘A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning.’ Archives of Computational Methods in Engineering. Vol. 27, pp. 1071-1092, 2022. 10.1007/s11831-019-09344-w
XXI. Wenjie Kang, Lan Lin, Baiwen Zhang, XiaoqiShen, ShuicaiWu, : ‘Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer’s disease diagnosis.’ Computers in Biology and Medicine. Vol. 136, 2021. 104678. 10.1016/j.compbiomed.2021.104678

XXII. Yawen, X., Jun, W., Zongil, L., Xiaodong, Z., : ‘A deep learning-based multi-model ensemble method for cancer prediction.’ Computer methods and programin Biomedicine. Vol. 153, pp. 1-9, 2018. 10.1016/j.cmpb.2017.09.005
XXIII. Yogesh, K.D., ElviraI Smagilova, Gert Aarts, et al., : ‘Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy.’ International Journal of Information Management. Vol. 57, 2021. 10.1016/j.ijinfomgt.2019.08.002
XXIV. Yuyang He, You Zhou, Tao Wen, Shuang Zhang, Fang Huang, XinyuZou, Xiaogang Ma, YueqinZhu, : ‘A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications.’ Applied Geochemistry. Vol. 140, pp. 105273, 2022. 10.1016/j.apgeochem.2022.105273
XXV. YuyanWang, Dujuan Wang, Na Geng, YanzhangWang, Y. Yunqiang, J. Yaochu, : ‘Stacking-based ensemble learning of decision trees for interpretable prostate cancer.’ Applied Soft Computing. Vol. 77, pp. 188-204, 2019. 10.1016/j.asoc.2019.01.015

View Download