Author/Authors :
Zhu, Fubao School of Computer and Communication Engineering - Zhengzhou University of Light Industry, Zhengzhou, Henan, USA , Li,Xiaonan School of Computer and Communication Engineering - Zhengzhou University of Light Industry, Zhengzhou, Henan, USA , Tang, Haipeng School of Computing Sciences and Computer Engineering - University of Southern Mississippi, Hattiesburg, MS, USA , He, Zhuo College of Computing - Michigan Technological University, Houghton, MI, USA , Zhang,Chaoyang School of Computing Sciences and Computer Engineering - University of Southern Mississippi, Hattiesburg, MS, USA , Hung, Guang-Uei Department of Nuclear Medicine - Chang Bing Show Chwan Memorial Hospital, Changhua, Taiwan , Chiu, Pai-Yi Department of Neurology - Show Chwan Memorial Hospital, Changhua, Taiwan , Zhou, Weihua College of Computing - Michigan Technological University, Houghton, MI, USA
Abstract :
Objective. The reliable diagnosis remains a challenging issue in the early stages of dementia. We aimed to develop and validate a new method based on machine learning to help the preliminary diagnosis of normal, mild cognitive impairment (MCI), very mild dementia (VMD), and dementia using an informant-based questionnaire. Methods. We enrolled 5,272 individuals who filled out a 37-item questionnaire. In order to select the most important features, three different techniques of feature selection were tested. Then, the top features combined with six classification algorithms were used to develop the diagnostic models. Results. Information Gain was the most effective among the three feature selection methods. The Naive Bayes algorithm performed the best (accuracy = 0.81, precision = 0.82, recall = 0.81, and F-measure = 0.81) among the six classification models. Conclusion. The diagnostic model proposed in this paper provides a powerful tool for clinicians to diagnose the early stages of dementia.
