Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction

Khan, Maryam Mahsal; Mendes, Alexandre; Chalup, Stephan K.

doi:10.1371/journal.pone.0192192

Cited by 37 publications

(14 citation statements)

References 45 publications

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“…Ali et al [20] studied the hand tremor abnormality detection associated with the risk of development of Parkinson's disease using a Chi2-based feature selection and Adaboostbased classification. Khan et al [36] proposed a method for the prediction of cancer and Parkinson's disease. e proposed method utilized the wavelet-based neural networks for the prediction of cancer.…”

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

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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.

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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

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“…91% [34], 95.38% [21], 62.61% [35], 85% [36],76.30% [37], 84% [38], 60.87% [35], 80.84% [39], 73.82% [37]. Cancer related Probabilistic models ( [40], [41]), Neural Networks([41]- [44]), SVM ( [45], [46]), Bayesian ( [47])…”

Section: Comparision and Performance Of Machine Learning Algorithmsmentioning

confidence: 99%

“…82.83% [48], 67.9% [41], 65.5% [41], 83.5% [42], 86% [43], 95.5% [44], 68% [45], 98.28% [46], 89% [47]. Depression Neural Networks ( [49], [50]), SVM ([51], [52]), Naive Bayes ( [53])…”

Section: Comparision and Performance Of Machine Learning Algorithmsmentioning

confidence: 99%

Healthcare analytics by engaging machine learning

Penikalapati

Rao

2020

SITech

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“…Artificial intelligence has already presented several works to solve problems of prediction of breast cancer. These works involve different resolutions through the identification of images [38][39][40][41], artificial neural networks [42][43][44][45][46][47], fuzzy approach [48,49], support vector machines [50,51], deep learning [52,53] among others.…”

Section: Related Workmentioning

confidence: 99%

Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer

Araújo

Guimarães

Souza

et al. 2019

MAKE

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Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals..

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Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction

Cited by 37 publications

References 45 publications

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

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

Healthcare analytics by engaging machine learning

Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer

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