Abstract
Parkinson's disease is a common neurodegenerative neurological disorder, which affects the patient's quality of life, has significant social and economic effects, and is difficult to diagnose early due to the gradual appearance of symptoms. Examining the discussion of Parkinson’s disease in social media platforms such as Twitter provides a platform where patients communicate each other in both diagnosis and treatment stage of the Parkinson’s disease. The purpose of this work is to evaluate and compare the sentiment analysis of people about Parkinson's disease by using deep learning and word embedding models. To the best of our knowledge, this is the very first study to analyze Parkinson's disease through social media by using word embedding models and deep learning algorithms. In this study, Word2Vec, GloVe, and FastText as word embedding models and Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short Term Memory Networks (LSTMs) as deep learning techniques are blended and used for classification purpose. Extensive experiments are conducted to analyze the sentiments of user comments about Parkinson's disease using word embedding models and deep learning algorithms on English Twitter dataset. The remarkable classification success with 75.12% of accuracy is observed in the experiments through the result of blending Word2Vec as a word embedding model and CNN as a deep learning technique. This study demonstrates the effectiveness of using word embedding models and deep learning algorithms to understand patients' needs, and provides a valuable contribution to the treatment process by analyzing the feelings of Parkinson's patients and their relatives through social media.
Highlights
Son yıllarda internet ve mobil teknolojinin gelişmesiyle birlikte, sosyal medya platformları hızla büyüdü
This study demonstrates the effectiveness of using word embedding models and deep learning algorithms to understand patients' needs, and provides a valuable contribution to the treatment process by analyzing the feelings of Parkinson's patients and their relatives through social media
The abbreviations SWE, RH, RU, STM and AOT are used for the preprocessing methods mentioned in Section 3.1, and Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs) are employed for deep learning algorithms
Summary
Parkinson Hastalığı (PD) hakkındaki çalışmaların literatür taramasının kısa bir özetini sunar. Probabilistic Neural Network (PNN) sınıflandırıcısı, Parkinson hastalığının tanısı için kullanılır ve %80.92'lik test başarısı elde eder. Hem sınıflandırma görevi hem de özellik seçimi için insan biyomedikal sesini içeren Parkinson hastalığı veri kümesi örneklerinde dilsel olarak güçlü adaptif sinir bulanık sınıflandırıcı (ANFC + LH) kullanmaktadırlar. Ağırlıklandırılmış Parkinson hasta veri kümesi için k-NN sınıflandırma algoritması kullanılarak farklı k değerleri için %97.93'lük bir başarı oranı elde edilmiştir [24]. Dört tıbbi veri seti, dermatoloji, Pima-Hint diyabeti, meme kanseri ve Parkinson veri kümesi kullanılarak, benzerlik sınıflandırıcısı (SC) ve bulanık entropi (FE) özellik seçim yöntemleriyle %85.03 sınıflandırma performansı elde edilmektedir [25]. Başka bir çalışmada [28] yazarlar, Parkinson hastalığında klinik ölçekler ve makine öğrenme modelleri kullanarak yeni ve geliştirilmiş aşama tahmini önermişlerdir. Parkinson hastaları için kişisel bir bakım planının geliştirilmesini kolaylaştırmayı ve sosyal medyadan gelen metin verilerini kullanarak, artan bir Parkinson hasta popülasyonuna kişisel sağlık bilgileri için değerli bir tıbbi kaynak sağlamayı önermekteyiz
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Pamukkale University Journal of Engineering Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.