Prediction of transition from mild cognitive impairment to Alzheimer's disease based on a logistic regression–artificial neural network–decision tree model

Abstract

AimTo develop a logistic regression model, artificial neural network (ANN) model and decision tree (DT) model for the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) to compare the performance of the three models.MethodsA total of 425 patients with MCI were screened from the original cohort. The actual follow up included 361 patients, with AD as the outcome variable. Three kinds of prediction models were developed: a logistic regression model, ANN model and DT model. The performance of all three models was measured with accuracy, sensitivity, positive predictive value and area under the receiver operating characteristic curve.ResultsA total of 121 patients with MCI developed AD, and the average conversion rate was 9.49% per year. The ANN model had higher accuracy (89.52 ± 0.36%), area under the receiver operating characteristic curve (92.08 ± 0.12), sensitivity (82.11 ± 0.42%) and positive predictive value (75.26 ± 0.86%) than the other two models. The first five important predictors of the ANN model were, in order, ADL score, age, urine AD‐associated neuronal thread protein, alcohol consumption and smoking. For the DT model, they were age, activities of daily living score, family history of dementia, urine AD‐associated neuronal thread protein and alcohol consumption. For the logistic regression model, they were age, sex, activities of daily living score, alcohol consumption and smoking.ConclusionThe logistic regression, ANN and DT models performed well at predicting the transition from MCI to AD with ideal stability. However, the ANN model had the best predictive value. Increased age, activities of daily living score, urine AD‐associated neuronal thread protein, alcohol consumption, smoking and sex were important factors. Geriatr Gerontol Int 2021; 21: 43–47.

Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis

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“…Although it represents a challenge, the high accuracy of the values of these ratios found in some studies reveals that their use is at least promising. 12 , 36 - 38 …”

The Use of the Artificial Neural Network for the Treatment Outcomes of Single-channel and Tri-channel Applicator Used in Cervical Cancer Based on High Dose Rate Brachytherapy

“…The AUC value of training and test set were 0.87 and 0.97, respectively. Kuang et al [32] proposed an ANN model for Alzheimer's disease, describing the sensitivity, speci city, and AUC were 82.11±0.42%, 75.26±0.86%, and 92.08±0.12%, respectively, included accuracy 89.52±0.36%. In our study, the AUC value was 0.961.…”

Development of a Sex-Specific Risk Scoring System for the Prediction of Cognitively Normal People to Patients With Mild Cognitive Impairment (SRSS-CNMCI)

“…Risk factors were selected as described in previous reports ( Stewart et al, 2009 ; Debette et al, 2011 ; Rönnemaa et al, 2011 ; Yang et al, 2011 ; Joas et al, 2012 ; Loy et al, 2014 ; Ben Bouallègue et al, 2017 ; Donohue et al, 2017 ; Gottesman et al, 2017 ; Abell et al, 2018 ; Alzheimer’s disease facts and figures, 2020 ; Guan et al, 2020 ; Suzuki et al, 2020 ; Kuang et al, 2021 ), including age, race, years of education, apolipoprotein E allele e4 ( APOE e4 ), family history of dementia (FHD), mini mental state examination (MMSE), the clinical dementia rating (CDR), systolic blood pressure and diastolic blood pressure.…”