The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model

Sonkurt H. O., ALTINÖZ A. E., Çimen E., KÖŞGER F., Öztürk G.

INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, vol.145, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 145
  • Publication Date: 2021
  • Doi Number: 10.1016/j.ijmedinf.2020.104311
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, CINAHL, Compendex, EMBASE, INSPEC, MEDLINE
  • Keywords: Bipolar disorder, Machine learning, Classification, Feature selection, ASSOCIATION, PERFORMANCE, PREVALENCE, DISEASE, CANTAB
  • Eskisehir Osmangazi University Affiliated: Yes


Background: Considering the clinical heterogeneity of the bipolar disorder, difficulties are encountered in making the correct diagnosis. Although a number of common findings have been found in studies on the neurocognitive profile of bipolar disorder, the search for a neurocognitive endophenotype has failed. The aim of this study is to separate bipolar disorder patients from healthy controls with higher accuracy by using a broader neurocognitive evaluation and a novel machine-learning algorithm.