Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques

• Published in: Scientific reports Vol. 11; no. 1; p. 20002
• Main Authors: Lin, Feng, Han, Jiarui, Xue, Teng, Lin, Jilan, Chen, Shenggen, Zhu, Chaofeng, Lin, Han, Chen, Xianyang, Lin, Wanhui, Huang, Huapin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.10.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/61; 692/617; Adult; Algorithms; Biomarkers; Brain - diagnostic imaging; Cognitive ability; Cognitive Dysfunction - diagnosis; EEG; Epilepsy; Epilepsy - pathology; Epilepsy - therapy; Female; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Magnetic Resonance Imaging; Male; multidisciplinary; Neuropsychological Tests; Outpatients; Patients; Prediction models; Prognosis; Risk Factors; Science; Science (multidisciplinary); Seizures; Statistical analysis; Treatment Outcome
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-99506-3

Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in outpatients with epilepsy. Data from 441 outpatients with epilepsy were included; of these, 433 patients met the 12 clinical characteristic criteria and were divided into training (n = 304) and experimental (n = 129) groups. After descriptive statistics were analyzed, cross-validation was used to select the optimal model. The random forest (RF) algorithm was combined with the redundancy analysis (RDA) algorithm; then, optimal feature selection and resampling were carried out after removing linear redundancy information. The features that contributed more to multiple outcomes were selected. Finally, the external traceability of the model was evaluated using the follow-up data. The RF algorithm was the best prediction model for both MMSE and MoCA outcomes. Finally, seven markers were screened by overlapping the top ten important features for MMSE ranked by RF modeling, those ranked for MoCA ranked by RF modeling, and those for both assessments ranked by RDA. The optimal combination of features were namely, sex, age, age of onset, seizure frequency, brain MRI abnormalities, epileptiform discharge in EEG and usage of drugs. which was the most efficient in predicting outcomes of MMSE, MoCA, and both assessments.