Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Dec 18, 2023)
Date Submitted: Sep 14, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Evaluation of Machine Learning Techniques for Classifying and Balancing Data on an Unbalanced Mini-Mental State Examination Test Results Datasets Applied in Chile
ABSTRACT
Background:
The Mini-Mental State Examination (MMSE) is the most widely used cognitive test for assessing whether suspected symptoms align with cognitive impairment or dementia. The results of this test are meaningful for clinicians but exhibit highly unbalanced distributions in studies and analyses regarding the classification of patients with cognitive impairment. This is a complex problem when a large number of MMSE tests are analysed. Therefore, data balancing and classification techniques are crucial to support decision-making in distinguishing patients with cognitive impairment in an effective and efficient manner.
Objective:
In this study, we explored machine learning techniques for data balancing and classification using a real unbalanced dataset consisting of MMSE test responses collected from 103 elderly patients participating in a Chilean patient monitoring project.
Methods:
We used eight data classification techniques and five data balancing techniques. We evaluated the performance of the techniques using the following metrics: sensitivity, specificity, F1-score, LR+, LR −, DOR, and AUC.
Results:
From the set of data balancing and classification techniques used in our study, the results indicate that the synthetic minority oversampling (SMOTE) and random forest balancing techniques improve the cognitive impairment diagnosis accuracy.
Conclusions:
The results obtained in our study support clinical decision-making regarding the early classification or exclusion of older adult patients with suspected cognitive impairment.
Citation
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