Journal Press India®

Computology: Journal of Applied Computer Science and Intelligent Technologies
Vol 4 , Issue 1 , January - June 2024 | Pages: 20-40 | Research Paper

Cognitive Impairment Diagnosis - A Game Theoretic Approach of Cognitive Machine Learning

Author Details ( * ) denotes Corresponding author

1. * Shardul Shastri, BCA Student, School of Computer Science, Engineering and Applications, D. Y. Patil International University, Pune, Maharashtra, India (shastrishar@gmail.com)
2. Sriya Mukkavillii, BCA Student, School of Computer Science, Engineering and Applications, D.Y. Patil International University, Pune, Maharashtra, India (sriya.mukkavilli@gmail.com)
3. Hetal Thaker, Assistant Professor, School of Computer Science Engineering and Application, D. Y. Patil International University, Pune, Maharashtra, India (hetal.jica@gmail.com)

Machine Learning (ML), a subset of Artificial Intelligence (AI) is used to simulate human-like intelligence in machines. Cognitive Machine Learning is a specialised branch of ML that focuses on developing algorithms to induce human-like reasoning and decision- making without explicit programming. Game theory explores mathematical models, used with statistical and behavioural analysis to predict outcomes in a social situation. Game theory can aid AI/ML training models with strategy identification and decision-making. To leverage insights from game theory and cognitive psychology, the analysis aims to emulate human-like decision-making and reason In AI systems. In this research paper, we analyse existing models, particularly focusing on Logistic Regression and Random Forest Algorithm to find an early diagnosis of cognitive impairment. By performing the comparative analysis, we aim to optimise the model selection process. New possibilities for intelligent systems are explored in this paper, demonstrating the synergy between AI, game theory, and cognitive psychology in tackling real-world challenges like cognitive impairment diagnosis.

Keywords

Game Theory, Machine Learning, Random Forest Algorithm, Cognitive Impairment Diagnosis, Logistic Regression

  1. Adelson, R. P., Garikipati, A., Maharjan, J., Ciobanu, M., Barnes, G., Singh, N. P., Dinenno, F. A., Mao, Q., & Das, R. (2024). Machine learning approach for improved longitudinal prediction of progression from mild cognitive impairment to Alzheimer’s disease. Diagnostics, 14(1), 13.
  2. Biau, G. (2012). Analysis of a random forests model. The Journal of Machine Learning Research, 13(1), 1063-1095.
  3. Breijyeh, Z., & Karaman, R. (2020). Comprehensive review on Alzheimer’s disease: Causes and treatment. Molecules, 25(24), 5789. Retrieved from https://doi.org/10.3390/ MOLECULES25245789
  4. Cramer, J. S. (2002). The Origins of Logistic Regression.
  5. Cycyk, L. M., & Wright, H. H. (2008). Frontotemporal dementia: Its definition, differential diagnosis, and management. Aphasiology, 22(4), 422–444. Retrieved from https://doi.org/10.1080/02687030701394598
  6. Dauwan, M., van der Zande, J. J., van Dellen, E., Sommer, I. E. C., Scheltens, P., Lemstra, A. W., & Stam, C. J. (2016). Random forest to differentiate dementia with Lewy bodies from Alzheimer’s disease. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, 4, 99–106. Retrieved from https://doi.org/10.1016/j.dadm.2016.07.003
  7. Frank, C. (2003). Dementia with Lewy bodies. Review of diagnosis and pharmacologic management. Canadian Family Physician, 49(10), 1304-1311.
  8. Hammoudeh, A., & Rlr, A. H. (n.d.). A concise introduction to reinforcement learning. Retrieved from https://www.studocu.com/in/document/amity-university/clinical-psycho logy/aconcise-introductionto-reinforcement-learning-2018-0209-research-gate/21000238
  9. Han, X., Zheng, X., Wang, Y., Sun, X., Xiao, Y., Tang, Y., & Qin, W. (2019). Random forest can accurately predict the development of end-stage renal disease in immunoglobulin a nephropathy patients. Annals of Translational Medicine, 7(11).
  10. Iwata, B. (2007). Negative reinforcement. Retrieved from https://homepages.se.edu/ cvonbergen/files/2013/01/Negative-Reinforcement-in-Applied-Behavior-Analysis_An-Emerging-Technology.pdf
  11. Lee, A. Y. (2011). Vascular Dementia. Chonnam Medical Journal, 47(2), 66. Retrieved from https://doi.org/10.4068/cmj.2011.47.2.66
  12. Li, J., & Gymrek, M. (2009). Theory of impartial games. Massachusetts Institute of Technology (MIT), 3.
  13. Lv, Z., Qiao, L., & Singh, A. K. (2021). Advanced machine learning on cognitive computing for human behavior analysis. IEEE Transactions on Computational Social Systems, 8(5), 1194–1202. Retrieved from https://doi.org/10.1109/TCSS.2020.3011158
  14. Maalouf, M. (2011). Logistic regression in data analysis: An overview. In International Journal of Data Analysis Techniques and Strategies, 3(3), 281–299. Retrieved from https://doi.org/10.1504/IJDATS.2011.041335
  15. Marek, M., Horyniecki, M., Frączek, M., & Kluczewska, E. (2018). Leukoaraiosis - New concepts and modern imaging. In Polish Journal of Radiology (Vol. 83, pp. e76–e81). Medical Science International. Retrieved form https://doi.org/10.5114/pjr.2018.74344
  16. Probst, P., & Bischl, B. (2019). Tunability: Importance of Hyperparameters of Machine Learning Algorithms. In Journal of Machine Learning Research (Vol. 20). Retrieved from http://jmlr.org/papers/v20/18-444.html.
  17. Ravindranath, V., & Sundarakumar, J. S. (2021). Changing demography and the challenge of dementia in India. Nature Reviews Neurology, 17(12), 747–758. Retrieved from https://doi.org/10.1038/s41582-021-00565-x
  18. Revathi, A., Kaladevi, R., Ramana, K., Jhaveri, R. H., Rudra Kumar, M., & Sankara P.K.M. (2022). Early detection of cognitive decline using Machine Learning algorithm and cognitive ability test. Security and Communication Networks. Retrieved from https://doi.org/10.1155/2022/4190023
  19. So, A., Hooshyar, D., Park, K. W., & Lim, H. S. (2017). Early diagnosis of dementia from clinical data by machine learning techniques. Applied Sciences, 7(7), 651.
Abstract Views: 6
PDF Views: 157

Related Articles
Effective Detection of Heart Disease Symptoms using Machine Learning
Gunji JaiSadhashiva, Shaik Mohammad Mohaboob Shareef, Devarakonda Aditya, Leela Venkat Muppavarapu, Dr. Senthil Athithan, Dr. B. Suneetha
Credit Default Prediction System Using Machine Learning
Hassan J. Bature, Daniel D. Wisdom, Tolulope T. Dufuwa, Isaac O. Ayetuoma
Development of an Intrusion Detection System using ANOVA Feature Selection and Support Vector Machine Algorithms
Mr. Michael F. Edafeajiroke, Dr. Sulaiman O. Abdulsalam, Mr. Mahmoud U. Shuaib, Dr. Ronke S. Babatunde
Enhancing Facial Detection and Recognition: Leveraging OpenCV and CNNs for Efficient Analysis
P. Srinadh, B. Sai Pavan, B. Naga Sai Chaitanya, V. Vidyadhar, G. R. Anantha Raman, Senthil Athithan
Heart Disease Prediction: A Comparative Analysis of Machine Learning Algorithms
Mr. Adarsh Sharma, Mr. Himanshu Sharma, Dr. Sudeep Varshney, Mrs. Nutan Gusain

By continuing to use this website, you consent to the use of cookies in accordance with our Cookie Policy.