[1C5-GS-13] AI application: Medical application (1)

[1C5-GS-13-02] Easy Screening of Rare Dementia by Ensemble Learning Acoustic Features from Speech

〇Shunya Hanai¹, Shohei Kato^1,2, Koichi Sakaguchi³, Takuto Sakuma¹, Reiko Ohdake⁴, Michihito Masuda⁴, Hirohisa Watanabe⁵ (1. Computer Science Program, Dept. of Engineering, Graduate School of Engineering, Nagoya Institute of Technology, 2. Frontier Research Institute for Information Science, Nagoya Institute of Technology, 3. Dept. of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology, 4. Department of Neurology, Nagoya University Graduate School of Medicine, 5. Department of Neurology, Fujita Medical University School of Medicine)

Keywords:Dementia, Frontotemporal lober degeneration, Alzheimer's disease, Speech analysis, Ensemble learning

In recent years, developed countries such as Japan have become a super-aging society, and a further increase in dementia patients is a serious problem. Dementia has different causes and treatments depending on the underlying disease, so it is important to diagnosis the disease correctly. However, some diseases are difficult to diagnosis by a general practitioner, and frontotemporal lobar degeneration (FTLD) is one of them. FTLD is a neurodegenerative disease that causes dementia and is a designated intractable disease in Japan. This disease has fewer cases than other dementia and is difficult to distinguish from Alzheimer's disease (AD). So, patients with suspected FTLD should be diagnosed by a specialist. Therefore, an easy screening is needed to refer patients with suspected FTLD to a specialist. In this study, we attempt to distinguish three groups of FTLD, AD, and healthy control (HC) using speech. We used ensemble learning to resolve the data imbalance, and classified by acoustic features extracted from speech. As a result, the above three groups were classified with 82% accuracy, 0.74 F-measure. Therefore speech analysis-based screening using ensemble learning is effective in classifying target diseases.