Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

Haq, Amin Ul; Li, Jian Ping; Memon, Muhammad Hammad; Khan, Jalaluddin; Malik, Asad; Ahmad, Tanvir; Ali, Amjad; Nazir, Shah; Ahad, Ijaz; Shahid, Mohammad

doi:10.1109/access.2019.2906350

Cited by 140 publications

(62 citation statements)

References 36 publications

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“…Later, many researchers as like in [26][27][28] worked on different voice data sets to improve the detection accuracy by feature engineering or applying different classifiers for developing telemonitoring systems. They also investigated different feature selection techniques to reduce dimensionality.…”

Section: Review On the Related Workmentioning

confidence: 99%

Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis

et al. 2020

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Background With the growing number of the aged population, the number of Parkinson’s disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients’ symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries. Method This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients’ feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress. Result The highest accuracy in PD detection using offline data was $$98.3\%$$ 98.3 % from voice data and $$98.5\%$$ 98.5 % from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient’s gender, we could improve the detection accuracy. This study’s novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes $$99.8\%$$ 99.8 % when ensemble averaging was performed on majority-vote from kNN, SVM, and NB. Conclusion The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population’s accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal.

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Section: Review On the Related Workmentioning

confidence: 99%

Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis

et al. 2020

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“…To evaluate our model, we calculated and compared the specificity, sensitivity and precision [11][12][13][14][15][16][17][18] of our model with that of experts' diagnosis over the test set.…”

Section: Methodsmentioning

confidence: 99%

Wavelet-Based Cough Signal Decomposition for Multimodal Classification

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Haq

et al. 2020

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

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Signal classifications have benefited from the successes of ML and DNN architectures. Cough classification techniques mainly extract features such as the Mel Frequency CepstralCoefficients for training. Most of these works also focus on obtaining information from single data modalities. However, multimodal analysis has been shown to aggregate useful information from different modalities thereby improving the internal capacity of ML models at data analysis. In this research, we propose a multimodal cough data classification approach with scalograms images obtained by decomposing cough signals using continuous wavelet transform and clinical information of subjects obtained from the COUGHVID dataset. Our result shows improved precision as compared to expert analysis.

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“…Machine learning algorithms play an important role in different areas of research [16,[25][26][27][28][29][30]. In this paper, a rough set approach is used for data modelling and user knowledge to extract meaningful insights.…”

Section: Rough Set Approach Toward Data Modelling and User Knowledge mentioning

confidence: 99%

Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

et al. 2021

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Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours (KNN), decision rules (DR), decomposition tree (DT), and local transfer function classifier (LTF-C) for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem.

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Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

Cited by 140 publications

References 36 publications

Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis

Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis

Wavelet-Based Cough Signal Decomposition for Multimodal Classification

Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

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