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

Tarakashar Das (), Sabrina Mobassirin (), Syed Md. Minhaz Hossain (), Aka Das (), Anik Sen (), Khaleque Md. Aashiq Kamal () and Kaushik Deb ()
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Tarakashar Das: Premier University, Chattogram
Sabrina Mobassirin: Premier University, Chattogram
Syed Md. Minhaz Hossain: Premier University, Chattogram
Aka Das: Premier University, Chattogram
Anik Sen: Premier University, Chattogram
Khaleque Md. Aashiq Kamal: Premier University, Chattogram
Kaushik Deb: Chittagong University of Engineering and Technology

Annals of Data Science, 2024, vol. 11, issue 5, No 15, 1864 pages

Abstract: Abstract 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.

Keywords: Parkinson’s disease (PD); Early diagnosis; Patient questionnaire (PQ); Artificial Neural Network (ANN); Feature selection (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s40745-023-00482-4

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