An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Cai, Zhennao; Gu, Jianhua; Wen, Caiyun; Zhao, Dong; Huang, Chunyu; Huang, Hui; Tong, Changfei; Li, Jun; Chen, Huiling

doi:10.1155/2018/2396952

Cited by 110 publications

(44 citation statements)

References 78 publications

(82 reference statements)

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“…[20] trained XG-Boost models to predict the changes in clinical scores of 51 PD patient using their phonation data. [21] employed chaotic bacterial foraging optimization (CBFO) with an enhanced fuzzy k-nearest neighbor (FKNN) classifier for early diagnostics of PD using vocal recording data. [22] proposed Modified Grey Wolf Optimization (MGWO) as a search strategy for feature selection, and Random forest, k-nearest neighbor classifier and decision tree for classification.…”

Section: Related Workmentioning

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

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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Section: Related Workmentioning

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

189

View full text Add to dashboard Cite

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“…According to the obtained results, the 400 features from the first level selection were used without ignoring any features and without any change, which achieved an accuracy of 93.3% classification-based PD detection. In addition, the probability of the PCA features existence in level two is: P(PCA) = 50/100 = 0.5 (22) Also, the probability of the existence of ECFS features in level two is:…”

Section: Discussion and Comparison With State-of-the-art Workmentioning

confidence: 99%

A Novel Framework of Two Successive Feature Selection Levels Using Weight-Based Procedure for Voice-Loss Detection in Parkinson’s Disease

et al. 2020

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Parkinson's disease (PD) is one of the public neuro-degenerative disorders. Speech/voice disorder is considered one of the symptoms at an early stage. Acoustic and speech signal processing methods can potentially evaluate and measure PD-related vocal impairment. The present work proposed a novel feature selection framework using two levels of the feature selection procedure for voice-loss detection in PD patients. At the first level selection, the principal component analysis (PCA) and the eigenvector centrality feature selection (ECFS) methods are initially calculated independently, and the selected features from each method are considered as a separated sublist, namely ECFS selected features sublist, and PCA selected features sublist, in the first set. Accordingly, the first set, which is the first level selection set, is generated from the union of these two sublists using the top-selected features from both methods. In the training phase, a second level selection, which forms the second set (which is a subset from the first set), is generated to calculate the proposed weight of each selection method. Since in the present work, the ECFS provided superior performance to the PCA in the first level selection, the ECFS is applied to the first set in order to find weight values based on the contribution/ impact of the top-selected PCA-and ECFS-features in the second level. This weight is determined by finding a proposed ratio, which is multiplied directly by the selected ECFS features in the first level. The selected weighted ECFS features are then combined with the same PCA features to avoid ignoring any of the top-ranked features from the first level. This combination includes the final weighted-hybrid selected features that fed to a support vector machine (SVM) classifier to evaluate the proposed weighted hybrid selected features. Hence, in the test phase, the generated weight is used directly without any further need for the second level selection. Several comparative studies were conducted to evaluate the proposed feature selection performance for PD voice-loss detection. The experimental results established the superiority of the proposed procedure using cubic kernel-SVM with 94% accuracy for voiceloss detection in PD, while, with the same classifier, 88% accuracy was achieved without using the proposed selection method.

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“…The authors claimed that the SVM classifier accomplished the best mean accuracy rate. Furthermore, authors in [8] used the fuzzy KNN approach on the dataset of Parkinson's disease and generated a diagnostic system that makes better decisions in clinical diagnosis. A statistical learning model was established in 2020 to help doctors forecast patients with Covid-19 for respiratory failure that requires mechanical ventilation.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

et al. 2021

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Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease’s risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

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An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Cited by 110 publications

References 78 publications

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

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

A Novel Framework of Two Successive Feature Selection Levels Using Weight-Based Procedure for Voice-Loss Detection in Parkinson’s Disease

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

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