Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods

Saeed, Faisal; Al-Sarem, Mohammed; Al-Mohaimeed, Muhannad; Emara, Abdel-Hamid M.; Boulila, ‪Wadii; Alasli, Mohammed; Ghabban, Fahad

doi:10.32604/cmc.2022.023124

Cited by 27 publications

(9 citation statements)

References 53 publications

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“…The model that achieved the best performance was KNN with 95% in the metrics of precision, accuracy, sensitivity, and F1 count. On the contrary, studies [36], [41], [42] obtained lower results than this study with the same model, achieving 88.33%, 91.18% and 88% accuracy, respectively. Differentiating mainly with the studies [36], [41], where they used a different data set than the one used in this study.…”

Section: Discussioncontrasting

confidence: 74%

“…The results show that the GB model achieved the best performance with 0.79 in precision and 0.7916 in accuracy, followed by ETC with 0.73 in precision and 0.75 in accuracy. Saeed et al [36], perform a comparative analysis of different ML models for early prediction of Parkinson's disease, for training the models they used a dataset storing 240 patient voice recordings. The training results positioned the KNN model as the best predictor with 0.8833 in accuracy.…”

Section: Review Of the Literaturementioning

confidence: 99%

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Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study

Zapata-Paulini,

Cabanillas-Carbonell

2024

IJEECS

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Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.

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Section: Discussioncontrasting

confidence: 74%

Section: Review Of the Literaturementioning

confidence: 99%

Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study

Zapata-Paulini,

Cabanillas-Carbonell

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…These two factors often disturb the results of classification accuracy in the field of medical science. Therefore, before using the data for disease classification in the medical system, it is important to use effective data preparation and data reduction techniques to find the most relevant risk factors [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

Talpur¹,

Abdulkadir²,

Hasan³

et al. 2023

Computers, Materials &Amp; Continua

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Machine learning (ML) practices such as classification have played a very important role in classifying diseases in medical science. Since medical science is a sensitive field, the pre-processing of medical data requires careful handling to make quality clinical decisions. Generally, medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features. These factors indirectly upset the disease prediction and classification accuracy of any ML model. To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature. However, the majority of such techniques frequently suffer from local minima issues due to large solution space. Thus, this study has proposed a novel wrapper-based Sand Cat Swarm Optimization (SCSO) technique as an FS approach to find optimum features from ten benchmark medical datasets. The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables. Moreover, K-Nearest Neighbor (KNN) classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm. The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds. The simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96% by selecting 14.2 features within 1.91 s. Additionally, the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSO-KNN method and six other algorithms for a p-value less than 5.00E-02. The findings revealed that the proposed algorithm produces better outcomes with an average p-value of 1.82E-02. Moreover, potential future directions are also suggested as a result of the study's promising findings.

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“…The reliability of software can be defined as an ability to perform the required functionality under certain conditions for a specific time [1]. Software reliability is itself a combination of few characteristics which are i) Maturity related to hardware, software or data, ii) Fault tolerance, and iii) Recoverability related to data, process and technology [2][3][4][5]. Although software reliability is significantly important for the quality of conventional software systems, web applications require another significant factor that is a software usability [6].…”

Section: Introductionmentioning

confidence: 99%

Measuring Reliability of A Web Portal Based on Testing Profile

Shahid¹,

Sulaiman²,

Al-Sarem³

et al. 2023

Computers, Materials &Amp; Continua

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Conventionally, the reliability of a web portal is validated with generalized conventional methods, but they fail to provide the desired results. Therefore, we need to include other quality factors that affect reliability such as usability for improving the reliability in addition to the conventional reliability testing. Actually, the primary objectives of web portals are to provide interactive integration of multiple functions confirming diverse requirements in an efficient way. In this paper, we employ testing profiles to measure the reliability through software operational profile, input space profile and usability profile along with qualitative measures of reliability and usability. Moreover, the case study used for verification is based on a web application that facilitates information and knowledge sharing among its online members.The proposed scheme is compared with the conventional reliability improvement method in terms of failure detection and reliability. The final results unveil that the computation of reliability by using the traditional method (utilizing failure points with the assistance of Mean Time Between Failures (MTBF) and Mean Time To Failure (MTTF) becomes ineffective under certain situations. Under such situations, the proposed scheme helps to compute the reliability in an effective way. Moreover, the outcomes of the study provide insight recommendations about the testing and measurement of reliability for Web based software or applications.

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Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods

Cited by 27 publications

References 53 publications

Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study

Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study

A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

Measuring Reliability of A Web Portal Based on Testing Profile

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