Details

Autor(en) / Beteiligte

• Das, Tarakashar
• Mobassirin, Sabrina
• Hossain, Syed Md. Minhaz
• Das, Aka
• Sen, Anik
• Kamal, Khaleque Md. Aashiq
• Deb, Kaushik

Titel

Patient Questionnaires Based Parkinson’s Disease Classification Using Artificial Neural Network

Ist Teil von

• Annals of data science, 2024-10, Vol.11 (5), p.1821-1864

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Parkinson’s disease is one of the most prevalent and harmful neurodegenerative conditions (PD). Even today, PD diagnosis and monitoring remain pricy and inconvenient processes. With the unprecedented progress of artificial intelligence algorithms, there is an opportunity to develop a cost-effective system for diagnosing PD at an earlier stage. No permanent remedy has been established yet; however, an earlier diagnosis helps lead a better life. Probably, the three most responsible categories of symptoms for Parkinson’s Disease are tremors, rigidity, and body bradykinesia. Therefore, we investigate the 53 unique features of the Parkinson’s Progression Markers Initiative dataset to determine the significant symptoms, including three major categories. As feature selection is integral to developing a generalized model, we investigate including and excluding feature selection. Four feature selection methods are incorporated—low variance filter, Wilcoxon rank-sum test, principle component analysis, and Chi-square test. Furthermore, we utilize machine learning, ensemble learning, and artificial neural networks (ANN) for classification. Experimental evidence shows that not all symptoms are equally important, but no symptom can be completely eliminated. However, our proposed ANN model attains the best mean accuracy of 99.51%, 98.17% mean specificity, 0.9830 mean Kappa Score, 0.99 mean AUC, and 99.70% mean F1-score with all the features. The efficiency of our suggested technique on diverse data modalities is demonstrated by comparison with recent publications. Finally, we established a trade-off between classification time and accuracy.