Title:Random-Forest-Algorithm-Based Applications of the Basic Characteristics
and Serum and Imaging Biomarkers to Diagnose Mild Cognitive Impairment
Volume: 19
Issue: 1
Author(s): Juan Yang, Haijing Sui, Ronghong Jiao, Min Zhang, Xiaohui Zhao, Lingling Wang, Wenping Deng and Xueyuan Liu*
Affiliation:
- Department of Neurology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai,
200092, People’s Republic of China
- Department of Neurology, Shanghai Pudong New Area People’s Hospital, Shanghai, 201299, China
Keywords:
Machine learning, algorithms, cognitive dysfunction, diagnostic tool, mild cognitive impairment, screening.
Abstract: Background: Mild cognitive impairment (MCI) is considered the early stage of
Alzheimer's Disease (AD). The purpose of our study was to analyze the basic characteristics and
serum and imaging biomarkers for the diagnosis of MCI patients as a more objective and accurate
approach.
Methods: The Montreal Cognitive Test was used to test 119 patients aged ≥65. Such serum biomarkers
were detected as preprandial blood glucose, triglyceride, total cholesterol, Aβ1-40,
Aβ1-42, and P-tau. All the subjects were scanned with 1.5T MRI (GE Healthcare, WI, USA) to obtain
DWI, DTI, and ASL images. DTI was used to calculate the anisotropy fraction (FA), DWI was
used to calculate the apparent diffusion coefficient (ADC), and ASL was used to calculate the cerebral
blood flow (CBF). All the images were then registered to the SPACE of the Montreal Neurological
Institute (MNI). In 116 brain regions, the medians of FA, ADC, and CBF were extracted by
automatic anatomical labeling. The basic characteristics included gender, education level, and previous
disease history of hypertension, diabetes, and coronary heart disease. The data were randomly
divided into training sets and test ones. The recursive random forest algorithm was applied to the
diagnosis of MCI patients, and the recursive feature elimination (RFE) method was used to screen
the significant basic features and serum and imaging biomarkers. The overall accuracy, sensitivity,
and specificity were calculated, respectively, and so were the ROC curve and the area under the
curve (AUC) of the test set.
Results: When the variable of the MCI diagnostic model was an imaging biomarker, the training accuracy
of the random forest was 100%, the correct rate of the test was 86.23%, the sensitivity was
78.26%, and the specificity was 100%. When combining the basic characteristics, the serum and
imaging biomarkers as variables of the MCI diagnostic model, the training accuracy of the random
forest was found to be 100%; the test accuracy was 97.23%, the sensitivity was 94.44%, and the
specificity was 100%. RFE analysis showed that age, Aβ1-40, and cerebellum_4_6 were the most
important basic feature, serum biomarker, imaging biomarker, respectively.
Conclusion: Imaging biomarkers can effectively diagnose MCI. The diagnostic capacity of the basic
trait biomarkers or serum biomarkers for MCI is limited, but their combination with imaging
biomarkers can improve the diagnostic capacity, as indicated by the sensitivity of 94.44% and the
specificity of 100% in our model. As a machine learning method, a random forest can help diagnose
MCI effectively while screening important influencing factors.
