Parkinson's disease (PD) is a progressive neurodegenerative disease that mainly affects older adults and is considered the second largest neurodegenerative disease disease after Alzheimer's disease. It is mainly characterized by motor and non-motor characteristics that affect the patient's movement, gait, balance and swallowing. Current research methods lack a comprehensive understanding of Parkinson's disease progression as several clinical and non-clinical factors are involved in the manifestation and progression of the disease which often lead to heterogeneity. Parkinson's disease has pathological manifestations that are much more complex to predict than motor/non-motor symptoms alone. One of the biggest concerns in predicting Parkinson's disease in the early stages is that the symptoms of the disease overlap with the symptoms of other diseases such as multiple sclerosis and Alzheimer's disease. Say no to plagiarism. Get a tailor-made essay on the topic "Why violent video games should not be banned"? Get an Original Essay The Unified Parkinson's Disease Rating Scale (UPDRS) is a widely used clinical symptom rating scale for Parkinson's disease and serves as a basic assessment tool to detect the presence and evaluate its severity. Although current medical approaches can reduce symptoms, there is no standard therapy to treat Parkinson's disease. Therefore, early diagnosis is of paramount importance to predict disease progression and help patients survive and improve quality of life. Furthermore, monitoring the progression of Parkinson's disease is often complicated as it involves a patient-directed care management plan in which disease symptoms and UPDRS scores are determined in clinic every 3, 6, or 12 months. Since most of the affected patients were elderly and the methods were time-consuming, this was not logistically and financially feasible for both doctors and patients. Furthermore, most methods currently used to monitor Parkinson's disease rely on expert clinical raters, by which assessment of Parkinson's disease symptoms can be difficult due to interindividual variability. Therefore, for all these reasons, periodic remote monitoring of Parkinson's disease progression and UPDRS scores is necessary. has emerged as an alternative solution to monitor Parkinson's disease patients with low-cost, non-invasive methods. Recognizing the importance of remote patient monitoring in Parkinson's disease, healthcare providers have developed and implemented several new organizational approaches to improve remote monitoring of disease progression in Parkinson's disease patients. Aside from motor and non-motor symptoms, speech impairment is prevalent in 70-90% of individuals with Parkinson's disease. Therefore, voice recordings would be useful for identifying Parkinson's disease and for monitoring the progression of the disease. Additionally, individuals with Parkinson's disease experience characteristic speech and language patterns, which could serve as potential indicators for early diagnosis. However, it is difficult for a clinician to manually characterize these speech patterns and voice recordings for the same patient or for multiple patients over the course of months. Machine learning (ML) methods programmed into wearable devices have emerged as a promising technology to automatically evaluate disease severity scores usingUPDRS scale. These methods apply statistical or mathematical algorithms on the input data to draw arbitrary patterns, common structures, or data points in the dataset to make predictions for new input (outcome) data. In this context, a systematic review of the available literature is needed to understand the development, application and validity of various machine learning algorithms to perform a comparative analysis that helps clinicians predict disease progression in Parkinson's disease and designers in the design of wearable devices. for remote monitoring of patients with Parkinson's disease. Although there is no specific test to diagnose Parkinson's disease, traditional diagnosis involves a neurologist examining the brain history and evaluating the subject's motor skills using various methods knowing that traditional methods are subject to interindividual variability. Another problem is that early symptoms of Parkinson's disease often overlap with symptoms of other diseases such as Alzheimer's disease, multiple sclerosis, Huntington's disease, and Lewy body dementia, leading to diagnostic errors. Due to the lack of standard laboratory tests or methods to diagnose Parkinson's disease, early diagnosis of the disease has become difficult where most motor symptoms are not severe in the early stages of the disease requiring regular monitoring of motor symptoms in clinical contexts. Parkinson's disease often occurs among older adults. adults and constant monitoring of the progression of the disease is guaranteed through regular clinical visits. Remote patient monitoring is gaining increasing attention to monitor disease progression using various non-invasive methods such as monitoring speech patterns in PD subjects. Since speech impairment is prevalent in 70-90% of subjects with Parkinson's disease, voice recordings would be useful in identifying Parkinson's disease and monitoring disease progression. Furthermore, individuals with PD experience characteristic speech and language patterns, which may serve as potential indicators for early diagnosis, low-cost, and time-consuming noninvasive diagnostic tools for PD. The reason for the increased attention to diagnosing Parkinson's disease using language models is mainly due to the rapid development of telediagnosis and telemonitoring in the medical field. Additionally, these methods are less expensive and the devices are often easy for subjects to self-monitor, which reduces patient visits to clinics by allowing patients to self-monitor disease progression. Although medications and surgeries can control the progression of Parkinson's disease by relieving motor symptoms, there is no one method to cure Parkinson's disease. Current research methods lack a comprehensive understanding of Parkinson's disease progression as several clinical and non-clinical factors are involved in the manifestation and progression of the disease which often lead to heterogeneity. Therefore, early diagnosis is of paramount importance to predict disease progression and help patients improve and survive in quality of life. The Unified Parkinson's Disease Rating Scale (UPDRS) is the universal and widely used clinical rating scale for assessing the clinical spectrum of PD and serves as the baseline assessment for PD. Understanding the relationship between UPDRS scores and patient speech signal characteristics has been extensively studied to predict Parkinson's disease in early stages. However, iDoctors cannot use UPDRS scores to manually evaluate and rate patient voice recordings in a large dataset. Since patients' voice recordings usually occupy a large space, it is almost impossible for doctors to evaluate them manually as it is a time-consuming process. Machine learning systems have emerged as a promising technology to automatically evaluate disease severity scores using the UPDRS scale. Therefore, computer-assisted systems that use machine learning algorithms are being developed to objectively detect and monitor disease progression. Most studies used advanced machine learning algorithms to extract the relevant or most significant features (feature extraction) from the database that contributes to PD (UPDRS scores). Voiced and unvoiced segments were extracted from the model constructed using the Gaussian mixture model (GMM-UBM) universal background model using the support vector regression algorithm. The model predicted PD with Pearson correlation of 0.60 for MDS-UPDRS scores. Remote monitoring of Parkinson's disease progression has been performed using regression methods such as Support Vector Machines (SVM), Least Square Support Vector Machines (LS-SVM), Multilayer Perceptron Neural Network (MLPNN), and General Regression Neural Network (GRNN) to predict the observed trend UPDRS scores. The results indicated that LS-SVM outperforms all other regression methods tested for the dataset. Minimum redundancy and maximum relevance feature selection algorithm tested on speech PD signals produced an accuracy of 90.3% and a precision of 90.2% and a Mathews correlation value of 0.73 using the random forest. This finding proved that simple random forest was better than other methods such as bagging, boosting, SVM, and decision tree methods. To support incremental data updates, the Incremental Support Vector Regression (ISVR) approach was implemented to predict UPDRS scores. The study confirms that self-organizing map (SOM), nonlinear iterative partial least squares (NIPALS), and ISVR techniques are effective in predicting Total-UPDRS and Motor-UPDRS (Nilashi et al. 2018). Although clustering was not the main focus of this study, advanced methods such as principal component analysis (PCA) and expectation maximization (EM) were performed to cluster the multi-colinear PD language data. New regression techniques such as adaptive neuro-fuzzy inference system (ANFIS) and SVR have been performed to predict PD progression, greatly improving the accuracy of PD prediction. However, this model was limited by a number of training samples in each cluster constructed by PCA and EM algorithms. The UPDRS evaluation for telemonitoring data was tested using linear and nonlinear regression techniques such as least squares (LS), Iteratively Reweighted LS (IRLS), and Least Absolute Shrinkage and Selection Operator (LASSO). Computational approaches such as neural networks could develop more accurate models for disease prediction. Artificial neural networks (ANN) and ANFIS have been used to predict PD using speech data. The advantages of using neural networks are that it is easy to train mapping models on input data. However, it has been noted that ANFIS models require relatively more time to train the model. The best model implemented in this paper yielded MAE = 5:33, MSE = 44:69, and R = 0:61. Data mining techniques.