Performance evaluation of combined feature selection and classification methods in diagnosing parkinson disease based on voice feature

Wibawa, Made Satria; Nugroho, Hanung Adi

doi:10.1109/icsitech.2015.7407790

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…The common classification metrices used to evaluate the performance of ANN model are, precession, recall (Sensitivity), Specificity which are calculated from confusion matrix, accuracy, F1-measure and ROC Curve (Wibawa et al, 2016) (Mohammed, 2020). The Confusion matrix contains TP (True Positive), TN (True Negative), FP (False Positive) and FN (False Negative).…”

Section: Evaluation Metrices (Recall Precession F Confusion Matrix Ro...mentioning

confidence: 99%

“…For the PD diagnosis, machine learning models have been applied to a collection of data sensory system, including handwritten diagrams (Drot, 2014) (Moshkova et al, n.d.) (Basnin & B, 2021), movement (Wahid et al, n.d.), neuroimaging (Wahid et al, n.d.) (Prashanth et al, 2014) (Quan et al, 2019) (Thakur et al, 2022), and voice (Wibawa et al, 2016)(T. J. (Pramanik & Sarker, 2021) (Ozcift, 2012) (Karapinar Senturk, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

2023

EAJSE

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Parkinson's Disease (PD) is a long-standing neurodegenerative condition of the central nervous system that mainly affects the motor system and origins full or partial damage in behavior, speech, motor reflexes, mental processing, and other energetic functions. Doctors use different types of datasets such as speech, movement and images from the people to diagnose the disease. In this paper, the speech dataset is collected from people with and without PD to detect the disease. The voice recording samples are analyzed and the feature vectors are extracted from the voice samples. A supervised ANN Multi-Layer Perceptron with a backpropagation algorithm is presented to accurately diagnose and distinguish between healthy and PD individuals. Different Architecture with diverse neuron numbers in the hidden layers are tested to utilize the model and the result of each architecture is compared to select the best ANN architecture for PD recognition. So far, our model score is the highest which is 93% for the testing dataset.

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Section: Evaluation Metrices (Recall Precession F Confusion Matrix Ro...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

2023

EAJSE

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“…Table A2 ( Appendix B ) explains the various statistical significance of the input features selection for the diagnosis of the PD and the performance parameter of various AI architectures [ 19 , 76 ]. The architecture uses a model with a classifier.…”

Section: Ranking Of Selected Studiesmentioning

confidence: 99%

“…Figure 1 shows various PD symptoms, namely, constipation problems, feelings of anxiety, depression, and abnormalities in breathing [ 18 ]. Other symptoms include difficulty in speaking [ 5 ], voice tone changes [ 17 ], and difficulty in swallowing food [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

et al. 2022

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Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

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“…This study focuses on the dataset that has various common speech features such as time-frequency, Melfrequency-cepstral-coefficients, wavelet-transform and vocal-fold have been explored comprehensively [12], [14], [23]. In addition to these features Tunable Q-Factor Wavelet Transform (TQWT) has also been utilized due to its discriminative properties and its provision of providing higher frequency resolutions compared to classic wavelet transform [29], [30].…”

Section: Introductionmentioning

confidence: 99%

Improved Classification with Simultaneous Feature Selection of Speech Signals for Parkinson's Patients

Sharif

Siddique

Mumtaz

et al. 2021

VFAST trans. softw. eng.

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Parkinson's disease (PD) is a brain disorder occurs due to a deficiency of dopamine hormone that regulates activities of the human body. Generally, the disease can be diagnosed by clinicians through clinical observation where they categorized PD patients on a PD assessment scale to understand disease severity in order to define a therapy/treatment plan. The clinicians have a view that this approach is not suitable for diagnosis at an early stage of the disease. Recent research outcome has shown that PD patients exhibit vocal impairment at the early stage of the disease, and this is now becoming a benchmark for early PD detection. Often researchers employ state-of-the-art speech analysis techniques that exploit various extracted features to meet the objective. An optimal set of features that best explains the problem often requires careful attention to the selection of extracted features in use. As a general practice, data analysts have a view that it is better to collect as many features as possible related to the problem but at the same time, it is also believed that the presence of some noisy features can also compromise classification ability. Our main objective in this work is to select/identify the optimal set of features to utilize for the machine learning classification models with an objective to have an improved early PD detection in patients. The selection of optimal features set will not only help clinicians to quickly diagnose PD but will also be useful to develop a better patient care strategy at an early stage of PD. In this study, various experiment are conducted to observe the most contributing speech feature to classify PD patients. The study have showed by using the Best-First feature selection approach the most optimal features from the PD dataset can be achieved. The efficacy of our approach with the optimal set of features has shown an improvement in classification with an accuracy of 92.19% that is better than the earliest reported accuracy of 86% [23] for an almost similar number of features.

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Performance evaluation of combined feature selection and classification methods in diagnosing parkinson disease based on voice feature

Cited by 14 publications

References 21 publications

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

Parkinson’s Disease Detection by Processing Different ANN Architecture Using Vocal Dataset

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

Improved Classification with Simultaneous Feature Selection of Speech Signals for Parkinson's Patients

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