The developments in engineering fields have extended the use of electromyography (EMG) beyond traditional diagnostic applications to multifarious areas like movement analysis. Surface EMG-based gesture recognition systems can provide the instinctive and exact recognition of various gestures with an effective classifier. Many researches have been done intensively on recognizing wrist and whole-hand gestures. Still, very little research was done on individual finger gestures, as this is considered more challenging due to the complexity and subtleness of muscle for individual finger movements. In this study, we tried to review surface EMG-based hand/finger movement recognition techniques using machine learning classifiers. We selected and evaluated 33 primary documents for this systematic literature review according to the Kitchenham methodology. Based on the research questions, this paper analyzes machine learning algorithms’ applicability, accuracy, and efficiency. It reviews the basic hand/finger gesture recognition techniques using a standard model. We also tried to identify the trends and gaps in the studied articles that could lead to new areas of study in the future.

The application of machine learning in the medical field is still limited. The main reason behind the lack of use is the unavailability of an easy-to-use machine learning system that targets non-technical users. The objective of this paper is to propose an automated machine learning system to aid non-technical users. The proposed system provides the user with simple choices to provide suggestions to the system. The system uses the combination of the user’s choices and performance evaluation to select the most suited model from available options.In this study, we employed the system on a Parkinson’s disease dataset. The templates for support vector machine and random forest algorithms are provided to the system. Support vector machines and random forests were able to produce 80% and 75% accuracy, respectively. The system used performance parameters of the system and user choices to select the most suited models for each test case. The support vector machine was selected as the most suited model in three test cases, while random forest was selected as the most suited for one test case. The test cases also showed that the weighted time parameter impacted the results heavily.

Diabetes mellitus is often a hyperglycemic condition that poses a substantial threat to human health. Early diabetes detection decreases morbidity and mortality. Due to the scarcity of labeled data and the presence of oddities in diabetes datasets, it is exceedingly difficult to develop a trustworthy and accurate diabetes prognosis. The dataset and groupings of the features using the elbow and silhouette methods have been clustered using K-means. Various machine learning approaches have also been applied to the cluster-based dataset to predict diabetes. We propose an unsupervised cluster-based feature grouping model for early diabetes identification using an open-source dataset containing the data of 520 diabetic patients. On the cluster-based dataset and the complete dataset, the maximum Accuracy (ACC) is 99.57% and 99.03%, respectively. The best Precision, Recall, minimum mean squared error (MSE), maximum mean squared error (MSE), and F1-Score of 1.000 are obtained from multi-layer perceptron (MLP), random forest (RF), and k-Nearest Neighbors (KNN), 0.984 from random forest (RF) and support vector machine (SVM), 0.010 from RF, 0.067 from KNN, and 99.20% from RF, respectively. A comparison table displays the anticipated outcomes and highlights the aspects of this research that are most likely to occur as intended. The preprocessed data and codes are available on the GitHub repository to https://github.com/mhashiq/Early-stage-diabetes-risk-prediction.