A Survey of Machine Learning Methods for Diagnosing Parkinsons Disease with Handwriting
Yazarlar (2)
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Gülşen Polat
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/ISMSIT58785.2023.10304996 | ||
| Kongre Adı | 7th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) | ||
| Kongre Tarihi | 26-10-2023 / | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Ankara |
| Bildiri Linki | http://dx.doi.org/10.1109/ismsit58785.2023.10304996 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| Parkinson's disease is a nervous system disorder for which early diagnosis is important. In recent years, promising results have been obtained for diagnosing Parkinson's disease with machine learning techniques. This review examines different studies on diagnosing Parkinson's disease using machine learning models and methods. Various methods, such as machine learning, deep learning, and transfer learning, classify Parkinson's disease. These models successfully distinguish Parkinson's patients from healthy individuals by using different data types, such as handwriting data and acoustic features. In this study, studies using handwriting for diagnosing Parkinson's were evaluated with a critical approach. It also informs the literature for future research in diagnosing Parkinson's disease using machine learning. |
| Anahtar Kelimeler |
| classification | deep learning | machine learning | Parkinson's disease | spiral drawings | transfer learning |
BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 2 |