A Pareto-Optimality-Based Approach for Selecting the Best Machine Learning Models in Mild Cognitive Impairment Prediction.
2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(2023)
Abstract
Mild Cognitive Impairment (MCI) is a syndrome charac-terized by cognitive impairment that is greater than expected for a subject's age and level of education. Nevertheless, it does not interfere with daily activity. Prevalence in epidemiological and population-based studies ranges from 3% to 19% in adults older than 65 years. A very interesting approach in this area is related to the identification of an Artificial Intelligence (Al)-based model and a subset of relevant features to predict the MCI clinical outcome. In our study, we propose a Pareto-optimality-based approach to identify the best model for predicting MCI. In fact, the best model achieves an Accuracy and Recall on Yes MCI of 71 % and 80% respectively. With this approach, it is possible to select the best model in order to predict Yes MCI (highest risk class). Our study presents a new best model selection approach that can be applied in identifying the best model that can be applied in various disease classification problems.
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Key words
MCI-Prediction,Machine Learning,Multi objective optimization
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