The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review

Salari, Nader; Kazeminia, Mohsen; Sagha, Hesam; Daneshkhah, Alireza; Ahmadi, Arash; Mohammadi, Masoud

doi:10.1007/s12144-022-02949-8

Cited by 10 publications

(10 citation statements)

References 98 publications

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“…It is sent from one node to the other via branches from each node. The selection is made in the tree based on the most recently visited leaf [12]. The critical reasoning in forming a decision tree structure can be explained as follows: at each node reached, the relevant questions are asked, and the final leaf is reached in the shortest amount of time and space possible based on the responses provided.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

“…The proposed model is constructed using the Dual Density 1-D Wavelet Transform method. Salari et al found high accuracy rates in different studies conducted recently [12]. A comprehensive data set was used in the studies, but balancing was not done.…”

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

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“…The major factor causing this condition is neurodegeneration of the dopaminergic system, which results in the downregulation of dopamine (DA) and an imbalance between the concentrations of acetylcholine and dopamine. 1 Numerous current therapy modalities reveal a failed recovery in Parkinson’s patients as a result of the disease’s incredibly complex nature. 2 PD is characterized by long-lasting euro-progressive cardinal indications, such as bradykinesia, tremors, postural disproportion, and muscular stiffness, as well as secondary symptoms like a disturbance in gait, poor walking, and difficulties speaking.…”

Section: Introductionmentioning

confidence: 99%

“…Parkinson’s disease (PD) symptoms include central nervous system disturbances that are chronic, severe, delayed-onset, degenerative disorders. The major factor causing this condition is neurodegeneration of the dopaminergic system, which results in the downregulation of dopamine (DA) and an imbalance between the concentrations of acetylcholine and dopamine . Numerous current therapy modalities reveal a failed recovery in Parkinson’s patients as a result of the disease’s incredibly complex nature .…”

Section: Introductionmentioning

confidence: 99%

Protective Effect of Hirsutidin against Rotenone-Induced Parkinsonism via Inhibition of Caspase-3/Interleukins-6 and 1β

et al. 2023

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A participant of the chemical family recognized as anthocyanins, hirsutidin is an O-methylated anthocyanidin. It is a natural substance, i.e., existing in Catharanthus roseus (Madagascar periwinkle), the predominant component in petals, as well as callus cultures. The literature review indicated a lack of scientifically verified findings on hirsutidin's biological activities, particularly its anti-Parkinson's capabilities. Using the information from the previous section as a reference, a present study has been assessed to evaluate the anti-Parkinson properties of hirsutidin against rotenone-activated Parkinson's in experimental animals. For 28 days, rats received hirsutidin at a dose of 10 mg/kg and rotenone at a dose of 0.5 mg/kg s.c. to test the neuroprotective effects. The hirsutidin was given 1 h before the rotenone. Behavioral tests, including the rotarod test, catalepsy, Kondziela's inverted screen activity, and open-field analysis, were performed. The levels of neurotransmitters (5-HT, DOPAC, 5-HIAA, dopamine, and HVA), neuroinflammatory markers (TNF-α, IL-6, IL-1β, caspase-3), an endogenous antioxidant, nitrite content, and acetylcholine were measured in all the rats on the 29th day. Hirsutidin exhibited substantial behavioral improvement in the rotarod test, catalepsy, Kondziela's inverted screen activity, and open-field test. Furthermore, hirsutidin restored neuroinflammatory markers, cholinergic function, nitrite content, neurotransmitters, and endogenous antioxidant levels. According to the study, hirsutidin has anti-inflammatory and antioxidant characteristics. As a result, it implies that hirsutidin may have anti-Parkinsonian effects in rats.

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“…The objective diagnosis approaches of NDDs are mostly based on neuroimaging, cognitive performance tests, language features, molecular and genetic data, neurophysiological and morphological biomarkers, and clinical records. 12 , 13 Utilizing a simple and reliable wearable system provides a possibility for predicting NDDs in daily life. In addition, both the lack of resources for face-to-face medical facilities and the COVID pandemic requires the remote diagnosis and monitoring of patients with NDDs in daily life.…”

Section: Introductionmentioning

confidence: 99%

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

et al. 2023

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Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

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The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review

Cited by 10 publications

References 98 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

Protective Effect of Hirsutidin against Rotenone-Induced Parkinsonism via Inhibition of Caspase-3/Interleukins-6 and 1β

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

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