Machine Learning Approaches for Detection and Diagnosis of Parkinson’s Disease - A Review

Nissar, Iqra; Mir, Waseem Ahmad; Izharuddin,; Shaikh, Tawseef Ayoub

doi:10.1109/icaccs51430.2021.9441885

Cited by 24 publications

(12 citation statements)

References 37 publications

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“…To classify a data set with objects, a size transformation is performed. [10]. The k nearest neighbor algorithm is created by calculating the neighborhood distances for each object.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

“…It has been stated that the conversion increases the accuracy rate, which is one of the performance criteria, albeit at a low rate. To get more relevant results, it is necessary to use more than one data set in a larger data set and balance the data set [10, 11] used the subjects’ walking data to diagnose PD. The data set was grouped according to the age factor.…”

Section: Introductionmentioning

confidence: 99%

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Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm

Al-Husban

Abdulridha

Mohamad

et al. 2022

Adsorption Science & Technology

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Neurodegenerative diseases drastically affect human beings without distinction; it does not matter if they are male or female. Sometimes, it is not clear why a person in their life developed a well-known disease in the world such as Parkinson’s disease (PD). Nowadays, various novel machine learning-based algorithms for evaluating Parkinson’s disease have been designed. The most recent strategy, which was developed using deep learning and can forecast the severity of Parkinson’s disease, is the one described here. To identify this disease, a thorough medical history, previous treatment history, physical examinations, and some blood tests and brain films must be completed. Diagnoses are more critical since they are less expensive and less time-consuming. Voice data from 253 people used in the current study corroborates the doctor’s diagnosis of Parkinson’s disease. To acquire the best results from the data, preprocessing is done. To perform the balancing procedure, a systematic sampling strategy was used to select the data that would be analyzed. Several data groups were constructed using a feature selection technique based on the label’s effect strength. Classification algorithms and performance evaluation criteria employ DT, SVM, and kNN. The classification algorithm and data group with the highest performance value were chosen, and the model was created due to this selection. The SVM approach was employed when constructing the model, and 45% of the original data set data were used. The data was sorted from most relevant to least important. 86% performance accuracy was achieved, in addition to excellent results in all other areas of the project. As a result, it has been established that medical decision support will be provided to the doctor with the assistance of the data set obtained from the speech recordings of the individual who may have Parkinson’s disease and the model that has been developed.

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“…To classify a data set with objects, a size transformation is performed. [10]. The k nearest neighbor algorithm is created by calculating the neighborhood distances for each object.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm

Al-Husban

Abdulridha

Mohamad

et al. 2022

Adsorption Science & Technology

View full text Add to dashboard Cite

show abstract

“… [16] and Nissar et al. [17] , which highlighted the possibilities of DL for pathological speech assessment, although datasets and accuracies are comparable to those obtained using ML methods for the same pathologies, namely Parkinson’s disease [18] and dysphonia [19] .…”

Section: Introductionmentioning

confidence: 88%

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures

Costantini

Cesarini

Robotti

et al. 2022

Knowledge-Based Systems

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“…3) The support vector machine Support Vector Machines are supervised learning algorithms that need labelled data in order to classify unknown data [20]. It operates using the idea of decision planes or hyperplanes to specify decision boundaries.…”

Section: ) Naive Bayesmentioning

confidence: 99%

Classification of Parkinson’s Disease using Speech Signal with Machine Learning and Deep Learning Approaches

Rahman

Hasan

Sarkar

et al. 2023

EJECE

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Parkinson's disease (PD) is a chronic neurological condition that is growing in prevalence and manifests both motor and non-motor symptoms. Most PD patients have trouble speaking, writing, and walking during the early stages of the disease. Analysis of speech problems has been effective in identifying Parkinson's disease. According to studies, 90% of Parkinson's disease patients experience speech problems. Even though there is no known cure for Parkinson's disease, using the right medication at an early stage can greatly reduce the symptoms. One of the key categorization issues for the diagnosis of Parkinson's disease is the correct interpretation of speech signals. The major goal of this project is to use deep learning and machine learning approaches to predict and categorize PD patients at an early stage. A trustworthy dataset from the UCI repository for Parkinson disease has been used to evaluate the method's performance. Several classification models are successfully used in this study for classification tasks, including Extreme Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, CatBoost, Gradient Boosting, Random Forest, Ridge, Decision Tree, Logistic Regression, K Neighbors, SVM - Linear Kernel, Naive Bayes, and deep neural networks (DNN1, DNN2, DNN3). The Extreme Gradient Boosting classifier achieved the greatest classification accuracy of 92.18% (among the machine learning classifiers). By using the chosen features as input, the three layer deep neural network (DNN2) has the best accuracy of 95.41% amongst deep learning techniques. The collected results indicate that deep neural networks performed better than machine learning methods.

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Machine Learning Approaches for Detection and Diagnosis of Parkinson’s Disease - A Review

Cited by 24 publications

References 37 publications

Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm

Biocomposite’s Multiple Uses for a New Approach in the Diagnosis of Parkinson’s Disease Using a Machine Learning Algorithm

Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures

Classification of Parkinson’s Disease using Speech Signal with Machine Learning and Deep Learning Approaches

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