A Multi-model Framework for Evaluating Type of Speech Samples having Complementary Information about Parkinson's Disease

Ali, Liaqat; Khan, Shafqat Ullah; Arshad, Muhammad; Ali, Sarfraz; Anwar, Muhammad

doi:10.1109/icecce47252.2019.8940696

Cited by 31 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 49 studies that used accuracy to evaluate machine learning models achieved an average accuracy of 90.9 (8.6) % ( Figure 4A ), ranging from 70.0% (Kraipeerapun and Amornsamankul, 2015 ; Ali et al, 2019a ) to 100.0% (Hariharan et al, 2014 ; Abiyev and Abizade, 2016 ; Ali et al, 2019c ; Dastjerd et al, 2019 ). In 3 studies, the highest accuracy was achieved by two types of machine learning models individually, namely regression or SVM (Ali et al, 2019a ), neural network or SVM (Hariharan et al, 2014 ), and ensemble learning or SVM (Mandal and Sairam, 2013 ). The per-study highest accuracy was achieved with SVM in 23 studies (39.7%), with neural network in 16 studies (27.6%), with ensemble learning in 7 studies (12.1%), with nearest neighbor in 3 studies (5.2%), and with regression in 2 studies (3.4%).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Mei

Desrosiers

Frasnelli

2021

Front. Aging Neurosci.

207

View full text Add to dashboard Cite

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Mei

Desrosiers

Frasnelli

2021

Front. Aging Neurosci.

207

View full text Add to dashboard Cite

show abstract

“…Recently, large number of diagnostic systems have been developed for automated diagnosis of different diseases like Parkinson's disease [ 15 – 19 ], hepatitis [ 20 ], carcinoma [ 21 ], lung cancer [ 22 ], and mortality prediction systems [ 23 , 24 ] using machine learning, deep learning [ 25 ], data mining [ 26 ], and optimization methods [ 27 – 30 ]. Heart disease detection through machine learning is not an exception, and recently, numerous approaches have also been successfully implemented on various datasets for automated heart disease detection [ 31 – 37 ].…”

Section: Machine Learning For Heart Disease Predictionmentioning

confidence: 99%

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Javeed

Khan

Ali³

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.

show abstract

“…ey obtained best performance of 57.5 using LOSO approach. Recently, Ali et al [44] proposed a multimodal approach under the LOSO approach and obtained unbiased performance of 70% classification accuracy using time frequency features. e phone was kept at a distance of 10 cm from each subject during recording of the voice phonations.…”

Section: Related Workmentioning

confidence: 99%

Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier

Rahman

Rizvi

Khan

et al. 2021

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

Parkinson’s disease (PD) is one of the most common and serious neurological diseases. Impairments in voice have been reported to be the early biomarkers of the disease. Hence, development of PD diagnostic tool will help early diagnosis of the disease. Additionally, intelligent system developed for binary classification of PD and healthy controls can also be exploited in future as an instrument for prodromal diagnosis. Notably, patients with rapid eye movement (REM) sleep behaviour disorder (RBD) represent a good model as they develop PD with a high probability. It has been shown that slight speech and voice impairment may be a sensitive marker of preclinical PD. In this study, we propose PD detection by extracting cepstral features from the voice signals collected from people with PD and healthy subjects. To classify the extracted features, we propose to use dimensionality reduction through linear discriminant analysis and classification through support vector machine. In order to validate the effectiveness of the proposed method, we also developed ten different machine learning models. It was observed that the proposed method yield area under the curve (AUC) of 88%, sensitivity of 73.33%, and specificity of 84%. Moreover, the proposed intelligent system was simulated using publicly available multiple types of voice database. Additionally, the data were collected from patients under on-state. The obtained results on the public database are promising compared to the previously published work.

show abstract

A Multi-model Framework for Evaluating Type of Speech Samples having Complementary Information about Parkinson's Disease

Cited by 31 publications

References 21 publications

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions

Parkinson’s Disease Diagnosis in Cepstral Domain Using MFCC and Dimensionality Reduction with SVM Classifier

Contact Info

Product

Resources

About