Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study

Araújo, Ana Camila Alves de; Santos, Enzo Gabriel Rocha; Sá, Karina Santos Guedes de; Furtado, Viviane Kharine Teixeira; Santos, Fernanda Araújo dos; Lima, Ramon Costa de; Krejcová, Lane Viana; Santos‐Lobato, Bruno Lopes; Pinto, Gustavo; Cabral, André dos Santos; Belgamo, Anderson; Callegari, Bianca; Kleiner, Ana Francisca Rozin; Silva, Anselmo de Athayde Costa e; Souza, Givago da Silva

doi:10.3389/fbioe.2020.00778

Cited by 24 publications

(22 citation statements)

References 39 publications

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“…Despite its widespread potential, the continuing success of flavor phenotyping depends upon an adequate supply of metabolomics data. An analysis of the test accuracies obtained from supervised machine learning algorithms showed that the machine learning phases significantly influenced the accuracies [ 67 ]. Our work included the evaluation of a range of statistical and machine learning models for the prediction of flavor quality based on metabolite information.…”

Section: Discussionmentioning

confidence: 99%

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

Sun

Zhao

et al. 2022

Foods

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Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as N-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.

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Section: Discussionmentioning

confidence: 99%

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

Sun

Zhao

et al. 2022

Foods

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“…This shows that most of the eHealth applications use the data obtained from the sensors such as accelerometer, gyroscopes, smart watch/smart phone/wearables, EEG/ECG/PPG etc. Accelerometer or tri-axial accelerometers, gyroscopes are used in more number of cases for the detection of the freezing of gait [28], bradykinesia [29], tremors [30] stroke [31] using machine learning algorithms, compared to other inertial sensors or gait sensors. Moreover, accelerometers can also be used for activity monitoring [32] when detecting the conditions of heart disease/diabetes.…”

Section: A Description Of Sensors Used In the Studiesmentioning

confidence: 99%

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

2021

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In this digitized world, data has become an integral part in any domain, including healthcare. The healthcare industry produces a huge amount of digital data, by utilizing information from all sources of healthcare, including the patients' demographics, medications, vital signs, physician's observations, laboratory data, billing data, data from various wearable sensors, etc. With the rapid growth of the wireless technology applications, there has also been a significant increase in the digital health data. New medical discoveries and new eHealth-related technologies, such as mobile apps, novel sensors, and wearable technology have contributed as important data sources for healthcare data. Nowadays, there is a huge potential to improve the healthcare quality and customer satisfaction with the help of machine learning (ML) algorithms applied on time-domain and frequency-domain healthcare data obtained from wearables and sensors. This systematic literature review examines in depth how health data from sensors can be processed and analyzed using ML techniques. The review focuses on the following diseases for obtaining the eHealth data: diabetes mellitus type 1 and type 2, hypertension and hypotension, atrial fibrillation, bradykinesia, dyskinesia, and fever related diseases. The data for the systematic literature review was collected from four databases, Medline, Proquest, Scopus, and Web of Science. We selected 67 studies for the final in-depth review out of the initial pre-selected papers. Our study identified that the major part of eHealth data is obtained from the sensors such as accelerometer, gyroscopes, ECG (Electrocardiogram), EEG (Electroencephalogram ) monitors, and blood glucose sensors. This study also examines the different feature types, feature extraction methods, and ML algorithms used for eHealth data analysis. Our review also shows that neural network (NN) algorithms and support vector machines (SVM) have shown so far the best performance for analyzing the healthcare data among other ML algorithms studied in the literature.

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“…Different types of widely used classification methods namely baseline classifiers are useful to explore various kinds of records and analyze their performance. After outlier detection and removal from primary and ANOVA Ftest, CSFS, MIFS, and RFE datasets, several widely used classifiers including Gaussian Naive Bayes (GNB) [34], [35], Logistic Regression (LR) [14], [36], Random Forest (RF) [37], [38], Decision Tree (DT) [22], Extreme Gradient Boosting (XGB) [39], [11], Gradient Boosting (GB) [23], K-Nearest Neighbour (KNN) [40], AdaBoost [41], Support Vector Machine (SVM) [21], Multi-layer Perceptron (MLP) [42], and Extra Trees (ET) [43] are used to investigate PD detection dataset more precisely.…”

Section: E Applying Baseline Classifiersmentioning

confidence: 99%

Machine Learning Model to Analyze Telemonitoring Dyphosia Factors of Parkinson’s Disease

Fahim¹,

Islam²,

Noor³

et al. 2021

IJACSA

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For many years, lots of people have been suffering from Parkinson's disease all over the world, and some datasets are generated by recording important PD features for reliable decision-making diagnostics. But a dataset can contain correlated data points and outliers that can affect the dataset's output. In this work, a framework is proposed where the performance of an original dataset is compared to the performance of its reduced version after removing correlated features and outliers. The dataset is collected from UCI Machine Learning Repository, and many machine learning (ML) classifiers are used to evaluate its performance in various categories. The same process is repeated on the reduced dataset, and some improvement in prediction accuracy is noticed. Among ANOVA F-test, RFE, MIFS, and CSFS methods, the Logistic Regression classifier along with RFEbased feature selection technique outperforms all other classifiers. We observed that our improved system demonstrates 82.94% accuracy, 82.74% ROC, 82.9% F-measure, along with 17.46% false positive rate and 17.05% false negative rate, which are better compared to the primary dataset prediction accuracy metric values. Therefore, we hope that this model can be beneficial for physicians to diagnose PD more explicitly.

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Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study

Cited by 24 publications

References 39 publications

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

Systematic Review on Machine-Learning Algorithms Used in Wearable-Based eHealth Data Analysis

Machine Learning Model to Analyze Telemonitoring Dyphosia Factors of Parkinson’s Disease

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