A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis

Souza, João Wellington M.; Alves, Shara S. A.; Rebouças, Elizângela de S.; Almeida, Jefferson S.; Filho, Pedro Pedrosa Rebouças

doi:10.1155/2018/7613282

Cited by 28 publications

(11 citation statements)

References 25 publications

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“…A structural Co-occurrence Matrix (SCM)-based technique to diagnose PD as shown in Figure 4 was proposed by [ 68 ]. In their research, the features were extracted from the spiral and meander handwriting exams of the Hand PD datasets [ 88 ].…”

Section: Adaptation Of the ML Frameworkmentioning

confidence: 99%

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

et al. 2022

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Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.

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Section: Adaptation Of the ML Frameworkmentioning

confidence: 99%

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

et al. 2022

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“…These tests are relevant because they are applied based on the first symptoms that arise in the individual with suspected PD. Handwriting tests can be conducted on paper, aided by computer vision systems [3,4]. Finger touch tests can be employed to evaluate tremor symptoms, such as for patients with Huntington's disease, which has similar symptoms to PD [5].…”

Section: Introductionmentioning

confidence: 99%

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Almeida

Filho

Carneiro

et al. 2019

Pattern Recognition Letters

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189

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This study investigates the processing of voice signals for detecting Parkinson's disease. This disease is one of the neurological disorders that affect people in the world most. The approach evaluates the use of eighteen feature extraction techniques and four machine learning methods to classify data obtained from sustained phonation and speech tasks. Phonation relates to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. The audio tasks were recorded using two microphone channels from acoustic cardioid (AC) and a smartphone (SP), thus allowing to evaluate the performance for different types of microphones. Five metrics were employed to analyze the classification performance: Equal Error Rate (EER) and Area Under Curve (AUC) measures from Detection Error Tradeoff (DET) and Receiver Operating Characteristic curves, Accuracy, Specificity, and Sensitivity. We compare this approach with other approaches that use the same data set. We show that the task of phonation was more efficient than speech tasks in the detection of disease. The best performance for the AC channel achieved an accuracy of 94.55%, AUC 0.87, and EER 19.01%. When using the SP channel, we have achieved an accuracy of 92.94%, AUC 0.92, and EER 14.15%.

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“…Then three classifiers are used to automatically identify PD, including SVM, Naive Bayes (NB) classifier and Optimum Path Forest (OPF). Finally, they achieved greater accuracy using SVM with RBF kernel and spiral handwriting compared to other models (de Souza et al, 2018). Sharma et al proposed a Grey Wolf as a search strategy for feature optimization (MGWO), and Random forest, K-NN and decision tree (DT) for classification.…”

Section: Shallow Learning For Parkinson Identificationmentioning

confidence: 99%

An optimized Parkinson's disorder identification through evolutionary fast learning network

Bouslah

Taleb

2021

IJICC

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PurposeParkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approachTo provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.FindingsThe proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/valueA novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.

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A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis

Cited by 28 publications

References 25 publications

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

An optimized Parkinson's disorder identification through evolutionary fast learning network

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