Cognitive Learning Approach to Enrich Understanding of Machine Learning on Healthcare Data

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Prasanna Palsodkar, Prachi Palsodkar, Yogita Dubey, Roshan Umate

10.14445/23488379/IJEEE-V11I8P102

How to Cite?

Prasanna Palsodkar, Prachi Palsodkar, Yogita Dubey, Roshan Umate, "Cognitive Learning Approach to Enrich Understanding of Machine Learning on Healthcare Data," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 8, pp. 14-5, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I8P102

Abstract:

Machine Learning (ML) has a significant impact on applications across various disciplines, with a key requirement of domain knowledge. A fully guided cognitive framework is presented in the case study for medical data analysis using the ML approach with an influence of the feature extraction effect, ensemble methods, and voting classifier using hyperparameter tuning. A case study-driven guided project design technique helps the student to comprehend the subject better. The learner gets familiar with reliable data sources and associated analysis jargon. The student is familiar with different intermediate steps, process flow, and legitimate conclusion dragging. The result shows that confidence in capstone project design in the relevant field and handling medical data is developed in learners. The learning removes hesitation of interdisciplinary work in the cognitive classroom. This strategy can successfully drive lifelong learning for all emerging computer science courses.

Keywords:

Cognitive learning, Diabetic, Ensemble, Healthcare, Machine learning, Project-based learning, Voting classifier.

References:

[1] Isidro Calvo et al., “A Multidisciplinary PBL Approach for Teaching Industrial Informatics and Robotics in Engineering,” IEEE Transactions on Education, vol. 61, no. 1, pp. 21-28, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[2] D.A. Umphress, T.D. Hendrix, and J.H. Cross, “Software Process in the Classroom: the Capstone Project Experience,” IEEE Software, vol. 19, no. 5, pp. 78-81, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Rodrigo Pessoa Medeiros, Geber Lisboa Ramalho, and Taciana Pontual Falcao, “A Systematic Literature Review on Teaching and Learning Introductory Programming in Higher Education,” IEEE Transactions on Education, vol. 62, no. 2, pp. 77-90, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Jack W. Smith et al., “Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus,” Proceedings of the Symposium on Computer Applications and Medical Care, pp. 261-265, 1988.
[Google Scholar] [Publisher Link]
[5] Timnit Gebru et al., “Datasheets for Datasets,” Communications of the ACM, vol. 64, no. 12, pp. 86-92, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Margaret Mitchell et al., “Model Cards for Model Reporting,” Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 220-229, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Karen L. Boyd, “Datasheets for Datasets Help ML Engineers Notice and Understand Ethical Issues in Training Data,” Proceedings of the ACM on Human-Computer Interaction, vol. 5, pp. 1-27, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Kasia S. Chmielinski et al., “The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence,” arXiv preprint arXiv:2201.03954, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ozlem Yavanoglu, and Murat Aydos, “A Review of Cyber Security Datasets for Machine Learning Algorithms,” 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, pp. 2186-2193, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Guillaume Lemaitre, Fernando Nogueira, and Christos K. Aridas, “Imbalanced-Learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning,” Journal of Machine Learning Research, vol. 18, no. 1, pp. 1-5, 2017.
[Google Scholar] [Publisher Link]
[11] Hima Patel et al., “Advances in Exploratory Data Analysis, Visualization and Quality for Data-Centric AI Systems,” Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4814-4815, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[12] El Kindi Rezig et al., “Towards an End-to-End Human-Centric Data Cleaning Framework,” Proceedings of the Workshop on Human-In-the-Loop Data Analytics, pp. 1-7, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Gavin C. Cawley, and Nicola L.C. Talbot, “On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation,” Journal of Machine Learning Research, vol. 11, pp. 2079-2107, 2010.
[Google Scholar] [Publisher Link]
[14] Abdelaziz Merghadi et al., “Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance,” Earth-Science Reviews, vol. 207, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Lars Kotthoff et al., “Auto-WEKA 2.0: Automatic Model Selection and Hyperparameter Optimization in WEKA,” Journal of Machine Learning Research, vol. 18, pp. 1-5, 2017.
[Google Scholar] [Publisher Link]
[16] Sebastian Raschka, “Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,” arXiv preprint arXiv:1811.12808, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jingwen Wang et al., “A Survey on Trust Evaluation Based on Machine Learning,” ACM Computing Surveys, vol. 53, no. 5, pp. 1-36, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Md. Kamrul Hasan et al., “Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers,” IEEE Access, vol. 8, pp. 76516-76531, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Samina Khalid, Tehmina Khalil, and Shamila Nasreen, “A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning,” 2014 Science and Information Conference, London, UK, pp. 372-378, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Kento Hasegawa, Masao Yanagisawa, and Nozomu Togawa, “Trojan-Feature Extraction at Gate-Level Netlists and Its Application to Hardware-Trojan Detection Using Random Forest Classifier,” 2017 IEEE International Symposium on Circuits and Systems, Baltimore, MD, USA, pp. 1-4, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Mehrbakhsh Nilashi et al., “Predicting Parkinson’s Disease Progression: Evaluation of Ensemble Methods in Machine Learning,” Journal of Healthcare Engineering, vol. 2022, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Sina Ardabili, Amir Mosavi, and Annamária R. Várkonyi-Kóczy, “Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods,” 18th International Conference on Global Research and Education, vol. 101, pp. 215-227, 2020. [CrossRef] [Google Scholar] [Publisher Link]
[23] Yong Zhang et al., ‘‘A Weighted Voting Classifier Based on Differential Evolution,” Abstract and Applied Analysis, vol. 2014, pp. 1-6, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Anam Yousaf et al., “Emotion Recognition by Textual Tweets Classification Using the Voting Classifier (LR-SGD),” IEEE Access, vol. 9, pp. 6286-6295, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Sandeep Trivedi, and Nikhil Patel, “The Determinants of AI Adoption in Healthcare: Evidence from Voting and Stacking Classifiers,” ResearchBerg Review of Science and Technology, vol. 1, no. 1, pp. 69-83, 2021.
[Google Scholar] [Publisher Link]