Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Bala, Neharika D; Anusuya, S

doi:10.35940/ijrte.f7129.038620

Cited by 6 publications

(2 citation statements)

References 10 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: 75%

“…The results reveal that the vowel "a" and the KNN offer an accuracy of 0.9118, positioning them as key elements in the effective detection of the disease. Similarly, in [42] with the use of voice recordings and body movement information of patients, they seek to predict the probability of developing Parkinson's disease. The results show that the KNN model achieved a better performance with 0.88 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: 75%

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

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Prediction of Parkinson’s Disease Using Machine Learning Models—A Classifier Analysis

Surya

Yaswanthram

Nair

et al. 2021

Lecture Notes in Networks and Systems

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Among the chronic nervous system diseases, Parkinson's disease (PD) is known for its progressiveness in impairing the speech ability, gait as well as complex muscle and nerve actions. Hence an early diagnosis of PD will help in reducing the symptoms. Telemedicine offers a cost-effective and convenient approach, and several studies have used dysphonic features to remotely detect PD. In this study, we have used a data set from Kaggle, which included voice measurements from 31 people of whom 23 were diagnosed with PD. The data set included 22 different attributes pertaining to voice measurements, including the pitch period entropy with 195 voice recordings for each of the individuals. In the data pre-processing, the correlated attributes were removed and we used 10 non-correlating attributes (< 0.7) along with individual status (0 and 1 for healthy and PD, respectively). The data set after preprocessing was split into 70:30 ratio and also ascertained that the number normal versus PD are in equal ratios in both the training and testing data sets, respectively. The data set was evaluated with four different supervised classification machine learning (ML) models, namely random forest, XGBoost, SVM and decision tree. The XGBoost classifier model was found to be highly efficient in precise classification of PD with an accuracy of 0.93.

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Comparative analysis of decision tree and naive bayes algorithm to predict Parkinson disease

Manoj,

Sherin Shibi,

Lakshmi

2024

Fifth International Conference on Applied Sciences: Icas2023

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Machine Learning Algorithms for Detection of Parkinson’s Disease using Motor Symptoms: Speech and Tremor

Cited by 6 publications

References 10 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

Prediction of Parkinson’s Disease Using Machine Learning Models—A Classifier Analysis

Comparative analysis of decision tree and naive bayes algorithm to predict Parkinson disease

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