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Optimal Ensemble Feature Selection (OEFS) Method and Kernel Weight Convolutional Neural Network (KWCNN) Classifier for Medical Datasets
Disease detection software that works automatically in healthcare domain refers to the proactive or reactive use of computerised data systems for diagnosis of diseases. Medical knowledge base, data processing, and data analytics are the three major components of the system. The procedures of data processing and data analytics are crucial. Data mining (DM) techniques were used to process these processes. DM is a tool for finding patterns in massive amounts of data and retrieving knowledge. Clinical and diagnostic evidence has created a slew of reliable timely detection services and other health-related technology in the DM and healthcare industries. Artificial Intelligence (AI) in Machine Learning (ML) includes classification and predictive analytics. Identifying key characteristics and developing a classification model to determine whether the cases are disease or not is a difficult task. Feature selection (FS) refers to the process of reducing the quantity of input features when developing a predictive model. Reducing the number of input features is desirable because it cuts the computational cost of modelling while also improving the model’s performance in some cases. Instead of using a single feature selection, Optimal Ensemble Feature Selection (OEFS) solves a feature selection problem by integrating numerous feature selections. The OEFS method works by integrating the outputs of different single feature selection models like Divergence Weight Elephant Herding Optimization (DWEHO), Divergence Weight ButterFly Optimization Algorithm (DWBFO), and Differential Evolution (DE). By merging different subsets of features, Weighted Majority Voting (WMV) is used in finding the optimal feature subset. Classification model using Kernel Weight Convolutional Neural Network (KWCNN) classification is proposed. The convolution operation is a mathematical linear action across matrices that gives it its name. In terms of medical disease diagnosis, the proposed KWCNN classification performs quite well. To determine the performance of all classification algorithms, evaluation criteria such as sensitivity, specificity, f-measure, and accuracy were measured using a confusion matrix.
Medical Diseases, Healthcare, Databases, Data Mining (DM), Artificial Intelligence (AI), Machine Learning (ML), Optimal Ensemble Feature Selection (OEFS), Divergence Weight Elephant Herding Optimization (DWEHO), Divergence Weight Butterfly Optimization Algorithm (DWBFO), Differential Evolution (DE), and Kernel Weight Convolutional Neural Network (KWCNN)
- S. De and B. Chakraborty, “Disease Detection System (DDS) using Machine Learning Technique”, Proceedings of International Conference on Machine Learning with Health Care Perspective, pp. 107-132, 2020.
- M. Fatima and M. Pasha, “Survey of Machine Learning Algorithms for Disease Diagnostic”, Journal of Intelligent Learning Systems and Applications, Vol. 9, No. 1, pp. 1-16, 2017.
- J.G. Richens, C.M. Lee and S. Johri, “Improving the Accuracy of Medical Diagnosis with Causal Machine Learning”, Nature Communications, Vol. 11, No. 1, pp.1-9, 2020.
- S. Chatterjee, K. Khunti and M.J. Davies, “Type 2 Diabetes”, The Lancet, Vol. 389, pp. 2239-2251, 2017.
- E.M. El Houby, “A Survey on Applying Machine Learning Techniques for Management of Diseases”, Journal of Applied Biomedicine, Vol. 16, No. 3, pp. 165-174, 2018.
- E. Dovgan, Y.C. Li and S. Syed Abdul, “Using Machine Learning Models to Predict the Initiation of Renal Replacement Therapy among Chronic Kidney Disease Patients”, Plos One, Vol. 15, No. 6, pp. 1-13, 2020.
- G. Ahmad, B.S. Khan and M.S. Aslam, “Automated Diagnosis of Hepatitis B using Multilayer Mamdani Fuzzy Inference System”, Journal of Healthcare Engineering, Vol. 2019, pp. 1-7, 2019.
- N.K. Kumar and D. Vigneswari, “Hepatitis-Infectious Disease Prediction using Classification Algorithms”, Research Journal of Pharmacy and Technology, Vol. 12, No. 8, pp. 3720-3725, 2019.
- G. Manogaran, R. Varatharajan and M.K. Priyan, “Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System”, Multimedia Tools and Applications, Vol. 77, No. 4, pp. 4379-4399, 2018.
- A.D. Dolatabadi, S.E.Z. Khadem and B.M. Asl, “Automated Diagnosis of Coronary Artery Disease (CAD) Patients using Optimized SVM”, Computer Methods and Programs in Biomedicine, Vol. 138, pp. 117-126, 2017.
- M. Abdar, M. Zomorodi-Moghadam, R. Das and I.H. Ting, “Performance Analysis of Classification Algorithms on Early Detection of Liver Disease”, Expert Systems with Applications, Vol. 67, pp. 239-251, 2017.
- J. Tang, S. Alelyani and H. Liu, “Feature Selection for Classification: A Review”, Proceedings of International Conference on Data Classification: Algorithms and Applications, pp.1-33, 2014.
- D. Guan, W. Yuan and M.K. Rasel, “A Review of Ensemble Learning based Feature Selection”, IETE Technical Review, Vol. 31, No. 3, pp. 190-198, 2014.
- V. Bolon Canedo and A. Alonso-Betanzos, “Ensembles for Feature Selection: A Review and Future Trends”, Information Fusion, Vol. 52, pp. 1-12, 2019.
- N. Hoque, M. Singh and D.K. Bhattacharyya, “EFS-MI: An Ensemble Feature Selection Method for Classification”, Complex and Intelligent Systems, Vol. 4, No. 2, pp. 105-118, 2018.
- M.J. Reddy and D.N. Kumar, “Computational Algorithms Inspired by Biological Processes and Evolution”, Current Science, Vol. 103, No. 4, pp. 370-380, 2012.
- P. Ghosh, F.J.M. Shamrat and A.A. Khan, “Optimization of Prediction Method of Chronic Kidney Disease using Machine Learning Algorithm”, Proceedings of International Joint Symposium on Artificial Intelligence and Natural Language Processing, pp. 1-6, 2020.
- J.S. Sartakhti, M.H. Zangooei and K. Mozafari, “Hepatitis Disease Diagnosis using a Novel Hybrid Method based on Support Vector Machine and Simulated Annealing (SVM-SA)”, Computer Methods and Programs in Biomedicine, Vol. 108, No. 2, pp. 570-579, 2012.
- D.C. Yadav and S. Pal, “Prediction of Heart Disease using Feature Selection and Random Forest Ensemble Method”, International Journal of Pharmaceutical Research, Vol. 12, No. 4, pp. 56-66, 2020.
- C.J. Qin, Q. Guan and X.P. Wang, “Application of Ensemble Algorithm Integrating Multiple Criteria Feature Selection in Coronary Heart Disease Detection”, Biomedical Engineering: Applications, Basis and Communications, Vol. 29, No. 6, pp. 1-13, 2017.
- M.S. Amin, Y.K. Chiam and K.D. Varathan, “Identification of Significant Features and Data Mining Techniques in Predicting Heart Disease”, Telematics and Informatics, Vol. 36, pp. 82-93, 2019.
- M. Nilashi, H. Ahmadi E. Akbari, “A Predictive Method for Hepatitis Disease Diagnosis using Ensembles of Neuro-Fuzzy Technique”, Journal of Infection and Public Health, Vol. 12, No. 1, pp. 13-20, 2019.
- V.R. Elgin Christo, B. Minu and A. Kannan, “Correlation-Based Ensemble Feature Selection using Bioinspired Algorithms and Classification using Backpropagation Neural Network”, Computational and Mathematical Methods in Medicine, Vol. 2019, pp. 1-17, 2019.
- O.A. Jongbo, T.A. Olowookere and A.O. Adetunmbi, “Performance Evaluation of an Ensemble Method for Diagnosis of Chronic Kidney Disease with Feature Selection Technique”, Proceedings of International Conference on Decision Aid Sciences and Application, pp. 959-965, 2020.
- C. Saranya and G. Manikandan, “A Study on Normalization Techniques for Privacy Preserving Data Mining”, International Journal of Engineering and Technology, Vol. 5, No. 3, pp. 2701-2704, 2013.
- Z. Liu, “A Method of SVM with Normalization in Intrusion Detection”, Procedia Environmental Sciences, Vol. 11, pp. 256-262, 2011.
- A. Kiran and D. Vasumathi, “Data Mining: Min-Max Normalization Based Data Perturbation Technique for Privacy Preservation”, Proceedings of International Conference on Computational Intelligence and Informatics, pp. 723-734, 2020.
- G.G. Wang, S. Deb and L.D.S. Coelho, “Elephant Herding Optimization”, Proceedings of International Symposium on Computational and Business Intelligence, pp. 1-5, 2015.
- S. Arora and S. Singh, “Butterfly Optimization Algorithm: A Novel Approach for Global Optimization”, Soft Computing, Vol. 23, No. 3, pp. 715-734, 2019.
- M. Tubishat, M. Alswaitti, S. Mirjalili and T.A. Rana, “Dynamic Butterfly Optimization Algorithm for Feature Selection”, IEEE Access, Vol. 8, pp. 194303-194314, 2020.
- M. Alweshah, “Solving Feature Selection Problems by Combining Mutation and Crossover Operations with the Monarch Butterfly Optimization Algorithm”, Applied Intelligence, Vol. 51, No. 6, pp. 4058-4081, 2020.
- W. Yi, Y. Zhou and J. Mou, “An Improved Adaptive Differential Evolution Algorithm for Continuous Optimization”, Expert Systems with Applications, Vol. 44, pp. 1-12, 2016.
- Y. Chen, W. Xie and X. Zou, “A Binary Differential Evolution Algorithm Learning from Explored Solutions”, Neurocomputing, Vol. 149, pp. 1038-1047, 2015.
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