Parkinson's disease (PD), the second most prevalent neurodegenerative disorder after Alzheimer's disease, affects approximately 10 million individuals worldwide. The disease is characterized by both motor and non-motor symptoms, and clinical aspects are pivotal for diagnosis. Vocal abnormalities can be identified in about 90% of PD patients in the early stages of the condition. Machine Learning (ML), a prominent subfield of Artificial Intelligence (AI), holds significant promise in the medical domain, particularly for early disease detection, enabling effective preventive measures and treatments. In this paper, we considered the unique characteristics of each ML algorithm. Seventeen ML algorithms were applied to a dataset of voice recordings from Healthy Control and PD individuals, sourced from a publicly available repository. We leveraged the PyCaret Python library's ML algorithms and functions, which were introduced in this article, to demonstrate their simplicity and effectiveness in dealing with real-world data. Among these algorithms, Extra Trees Classifier (ETC), Gradient Boosting Classifier (GBC), and K Neighbors Classifier (KNN) exhibited the best performance for the given dataset. Furthermore, to enhance the models' performance, we employed various techniques, including Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, feature selection based on correlation, and hyperparameter tuning. Our findings highlight the potential of the PyCaret ML library demonstrated in this article as a valuable tool for applying ML to the classification of Parkinson's disease through voice analysis. The application of ML in this context can greatly support clinical decision-making, leading to more informed and precise interventions.

Parkinson’s disease (PD) is a neurological condition that affects approximately 10 million individuals globally and is ranked as the second most prevalent neurodegenerative condition after Alzheimer’s disease. Vocal disorders can be identified in approximately 90% of PD patients in the early stages of the disease. In this study, 19 machine learning (ML) algorithms were applied to a database of voice recordings of healthy individuals and individuals with PD obtained from a public repository. Different feature selection (FS) and hyperparameter optimization techniques were applied to all models for training, testing, and validation data. Among the ML algorithms, support vector machine with radial kernel, Naïve Bayes (NB), and Gaussian process classifier (GPC) yielded promising results when considering all features. Linear discriminant analysis, K neighbors classifier (KNN), extra trees classifier, GPC, and NB demonstrated excellent performance on the testing data after employing FS techniques. Decision tree classifier, KNN, and GPC emerged as the top performers when applied to the validation dataset. Our findings, derived from an extensive and chronological review of studies utilizing the same dataset, which surpass previous benchmarks, provide a comprehensive understanding of ML’s application in voice analysis to support accurate clinical decision-making.

Background: Rigidity is a cardinal symptom of Parkinson’s Disease (PD), yet its clinical evaluation remains largely subjective and susceptible to errors. This study introduces an innovative method for objectively classifying individuals with PD by combining an active wrist orthosis with Machine Learning (ML) models. Methods: The orthosis, equipped with current and force sensors, recorded biomechanical signals during passive wrist flexion and extension, from which twelve quantitative features were extracted. Data were collected from 30 participants (15 with PD and 15 Healthy Controls). Nineteen supervised ML algorithms were systematically evaluated through feature selection, cross-validation, and hyperparameter tuning. Results: Using all twelve features, QDA achieved an accuracy of 0.889 and sensitivity of 1.000, followed by GPC (0.778) and LDA (0.778). After applying feature selection with the Correlation-based Feature Subset to reduce redundancy, Extra Trees reached 0.833 accuracy, while both QDA and GPC maintained accuracies of 0.778. This consistency across models, even with a reduced feature set, highlights the robustness of the extracted biomarkers. Conclusions: These findings confirm that wrist rigidity signals provide discriminative quantitative information between PD patients and HC and are able to support PD classification, combining engineering innovation with clinical practice that highlights the potential of integrating wearable devices and ML as a personalized healthcare in PD.

Parkinson’s disease (PD) is a neurological condition affecting millions, marked by mobility issues and characterized by motor and non-motor symptoms, including tremors, bradykinesia, postural instability, and rigidity. Diagnosis often relies on subjective assessments such as the Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS). This study focuses on validating a wrist orthosis designed to quantify rigidity in PD patients objectively. Developed at the Center for Innovation and Technological Evaluation in Health (NIATS), the orthosis integrates a Faulhaber linear motor (LM 2070-080-11) and microcontroller (MCLM 3006 S RS). Calibration experiments, including varied mass assessments, established the orthosis’s reliability. Results indicated a newly calculated force constant of 14.28 N/A, 18.49% higher than the manufacturer’s value, with a strong Pearson correlation coefficient (0.9997189). The orthosis detected masses ranging from 39.07 to 812.64 grams without yielding. Angular displacement calibration, utilizing a GP10 goniometer and Myosystem-Br1 software, demonstrated linearity, supported by Pearson coefficients of 0.9995091 and 0.995259. These findings underscore the orthosis’s potential as a reliable tool for measuring rigidity in PD patients, promising advancements in physiotherapy and disease monitoring.

Early diagnosis of Parkinson’s disease (PD) is a clinical challenge due to the non-specific nature of the initial motor symptoms (MS) and the limitations of traditional diagnostic methods. Hand-drawn tasks, such as Archimedes’ spiral, have emerged as potential tools for assessing motor impairment. This study aimed to employ computer vision and machine learning (ML) techniques, with an emphasis on the use of DeepLabCut (DLC), to analyze drawing patterns of individuals with PD and healthy controls. Twenty individuals (10 with PD and 10 healthy controls), matched for age and gender, participated in the study. Each participant completed 12 manual drawings, totaling 240 samples recorded on video. The recordings were processed in DLC to extract spatial coordinates, and the resulting signals were preprocessed and normalized. Eighty-five variables related to amplitude, frequency, entropy, statistical properties, and displacement were extracted. For classification, attribute selection techniques and 14 supervised ML algorithms were applied, using cross-validation (CV) for performance evaluation. The ML models applied to the drawing patterns demonstrated high classification ability, with an accuracy of approximately 93%. The Extra Trees Classifier (ETC) showed the highest performance, reaching an AUC of 0.98, followed by K-Neighbors with an AUC of 0.97. Attribute selection contributed to performance optimization, and CV strategies ensured the robustness of the results. These findings highlight the potential of DLC and ML as a non-invasive tool for early detection and monitoring of motor impairment in Parkinson’s disease.