Journal Press India^®

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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

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