Analysis of neuropsychological and neuroradiological features for diagnosis of Alzheimer's disease and mild cognitive impairment

Vladimir Kurbalija; Zoltan Geler; Tijana Vujanić Stankov; Igor Petrušić; Mirjana Ivanović; Igor Kononenko; Marija Semnic; Marko Daković; Robert Semnic; Zoran Bosnić

doi:10.1016/j.ijmedinf.2023.105195

Analysis of neuropsychological and neuroradiological features for diagnosis of Alzheimer's disease and mild cognitive impairment

Int J Med Inform. 2023 Oct:178:105195. doi: 10.1016/j.ijmedinf.2023.105195. Epub 2023 Aug 11.

Authors

Affiliations

¹ University of Novi Sad, Faculty of Sciences, Department of Mathematics and Informatics, Trg D. Obradovića 4, 21000 Novi Sad, Serbia.
² University of Novi Sad, Faculty of Philosophy, Department of Media Studies, Dr Zorana Đinđića 2, 21000 Novi Sad, Serbia. Electronic address: zoltang@ff.uns.ac.rs.
³ University of Novi Sad, Faculty of Medicine, Department of Neurology, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; University Clinical Centre of Vojvodina, Neurology Clinic, Hajduk Veljkova 1, 21000 Novi Sad, Serbia.
⁴ University of Belgrade, Faculty of Physical Chemistry, Laboratory for Advanced Analysis of Neuroimages, Studentski trg 12-16, 11000 Belgrade, Serbia.
⁵ University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia.
⁶ Uppsala University, Department of Surgical Sciences, Radiology, P.O. Box 256, SE-751 05 Uppsala, Sweden.

PMID: 37611363
DOI: 10.1016/j.ijmedinf.2023.105195

Abstract

Background: Age-related neurodegenerative diseases are constantly increasing with prediction that in 2050 over 60 % of population will suffer from some level of cognitive impairment. A cure for the Alzheimer's disease (AD) does not exist, so early diagnosis is of a great importance. Machine learning techniques can help in early diagnosis with deep medical data processing, disease understanding, intervention analysis and knowledge discovery for achieving better medical decision making.

Methods: In this paper, we analyze the dataset consisting of 90 individuals and 482 input features. We investigate the achieved AD prediction performances using seven classifiers and five feature selection algorithms. We pay special focus on analyzing performance by utilizing only a subset of best ranked attributes to establish the minimum amount of input features that ensure acceptable performance. We also investigate the significance of neuropsychological (NP) and neuroradiological (NR) attributes for the AD diagnosis.

Results: The accuracy for the whole set of attributes ranged between 66.22 % and 81.00 %, and the weighted average AUROC was between 76.3 % and 95.0 %. The best results were achieved by the naive Bayes classifier and the Relief feature selection algorithm. Additionally, Support Vector Machines classifier shows the most stable results since it depends the least on the feature selection algorithm which is used. As the main result of this paper, we compare the performance of models trained with automatically selected features to models trained with hand-selected features performed by medical experts (NP and NR features).

Conclusions: The results reveal that unlike the NR attributes, the NP attributes achieve a good performance that is comparable to the full set of attributes, which suggests that they possess a high predictive power for AD diagnosis.

Keywords: Alzheimer's disease; Classification; Feature selection; Machine learning.