Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Journal: Journal of medical systems
PMID: 30368611

Abstract

Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic information based on causal and/or statistical data and therefore reveal hidden dependencies between symptoms and illnesses. In this paper we give a detailed overview of the recent machine learning research and its applications for predicting cognitive diseases, especially the Alzheimer's disease, mild cognitive impairment and the Parkinson's disease. We survey different state-of-the-art methodological approaches, data sources and public data, and provide their comparative analysis. We conclude by identifying the open problems within the field that include an early detection of the cognitive diseases and inclusion of machine learning tools into diagnostic practice and therapy planning.

Authors

  • Brankica Bratić
    Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 4, Novi Sad, Serbia. brankica.bratic@dmi.uns.ac.rs.
  • Vladimir Kurbalija
    Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 4, Novi Sad, Serbia.
  • Mirjana Ivanović
    Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovića 4, Novi Sad, Serbia.
  • Iztok Oder
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia.
  • Zoran Bosnić
    Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia.

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