Abstract—Mild Cognitive Impairment (MCI) is a syndrome characterized 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 (AI) based model and a subset of relevant features to predict the MCI clinical outcome. In our study, we propose a Pareto-optimalitybased 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 mode.
A Pareto-Optimality-Based Approach for Selecting the Best Machine Learning Models in Mild Cognitive Impairment Prediction / Sorino, P.; Paparella, V.; Lof(\`u), D.; Colafiglio, T.; Di Sciascio, E.; Narducci, F.; Sardone, R.; Di Noia, T.. - (2023), pp. 3822-3827. ( IEEE Hawaii ) [10.1109/SMC53992.2023.10394057].
A Pareto-Optimality-Based Approach for Selecting the Best Machine Learning Models in Mild Cognitive Impairment Prediction
Colafiglio, T.
Membro del Collaboration Group
;Sardone, R.Membro del Collaboration Group
;
2023
Abstract
Abstract—Mild Cognitive Impairment (MCI) is a syndrome characterized 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 (AI) based model and a subset of relevant features to predict the MCI clinical outcome. In our study, we propose a Pareto-optimalitybased 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 mode.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
