Open Access
Issue
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
Article Number 02028
Number of page(s) 9
Section Machine Learning Applications in Vision, Security, and Healthcare
DOI https://doi.org/10.1051/itmconf/20257802028
Published online 08 September 2025
  1. R., V.C., Asha, V., Prasad, A., Das, S., Kumar, S., and P., S.S.: 'Support vector machine (SVM) and artificial neural networks (ANN) based chronic kidney disease prediction', Proc. 7th Int. Conf. Comput. Methodol. Commun. (ICCMC), Erode, India, 2023, pp. 469–474 [Google Scholar]
  2. Shen, L., Jin, Y., Pan, A., Wang, K., Ye, R., Lin, Y., Anwar, S., Xia, W., Zhou, M., and Guo, X.: 'Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery', Comput. Methods Programs Biomed., 2025, 260, 108561 [Google Scholar]
  3. Sumwiza, K., Twizere, C., Rushingabigwi, G., Bakunzibake, P., and Bamurigire, P.: 'Enhanced cardiovascular disease prediction model using random forest algorithm', Inform. Med. Unlocked, 2023, 41, 101316 [Google Scholar]
  4. Asmae, O., Abdelhadi, R., Bouchaib, C., Sara, S., and Tajeddine, K.: 'Parkinson’s disease identification using KNN and ANN algorithms based on voice disorder', Proc. 1st Int. Conf. Innovative Res. Appl. Sci., Eng. Technol. (IRASET), 2020, pp. 1–6 [Google Scholar]
  5. Annabel, L.S.P., Sreenidhi, S., and Vishali, N.: 'A novel diagnosis system for Parkinson’s disease using K-means clustering and decision tree', in Communication and Intelligent Systems (Springer, Berlin/Heidelberg, Germany, 2021), pp. 607–615 [Google Scholar]
  6. Battineni, G., Chintalapudi, N., Amenta, F., et al.: 'A comprehensive machine-learning model applied to magnetic resonance imaging (MRI) to predict Alzheimer’s disease (AD) in older subjects', J. Clin. Med., 2020, 9, (7), 2146 [Google Scholar]
  7. Fisher, C.K., Smith, A.M., Walsh, J.R., et al.: 'Machine learning for comprehensive forecasting of Alzheimer’s disease progression', Sci. Rep., 2019, 9, (1), 13622 [Google Scholar]
  8. Huang, Z., Sun, M., and Guo, C.: 'Automatic diagnosis of Alzheimer’s disease and mild cognitive impairment based on CNN + SVM networks with end-to-end training', Comput. Intell. Neurosci., 2021, 2021, 9121770 [Google Scholar]
  9. El Kharoua, R.: 'Alzheimer's disease dataset', https://www.kaggle.com/datasets/rabieelkharoua/alzheimers-disease-dataset/data, accessed 14 February 2025 [Google Scholar]
  10. Sun, Z., Wang, G., Li, P., Wang, H., Zhang, M., and Liang, X.: 'An improved random forest based on the classification accuracy and correlation measurement of decision trees', Expert Syst. Appl., 2024, 237, 121549 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.