Classification of Parkinson’s Disease Data Using Traditional and Advanced Data Mining Techniques

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

Heart disease is still one of the top causes of death globally, demanding early and effective prediction tools. Traditional predictive models frequently have limitations because they rely on single‐source statistics, which may not represent the entire scenario of a patient's complex health profile. Feature selection is an essential preprocessing step in machine learning that improves model efficiency by eliminating irrelevant or redundant features. This study evaluates the impact of feature selection on six classification modelsNaïve Bayes, Logistic Regression, Decision Tree, K‐Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forestacross four datasets. Performance was assessed using accuracy, precision, recall, F1‐score, and area under the curve (AUC) before and after feature selection. The results indicate that feature selection had varying effects across different models and datasets. In Dataset‐01, Random Forest maintained its strong performance (98.70% accuracy) even after feature selection, while SVM and KNN saw noticeable drops, with accuracy decreasing from 88.96% to 79.87% and 87.01% to 81.17% respectively. For Dataset‐02, SVM showed improvement, increasing from 80.00% to 85.00%, while Naïve Bayes and KNN also improved, reaching 82.50% and 82.50% accuracy, respectively. In Dataset‐03, Logistic Regression and KNN experienced declines, with accuracy dropping from 88.76% to 83.71% and 91.57% to 87.08%, respectively, while Random Forest fell from 97.75% to 91.57%. Dataset‐04 showed mixed results, with Logistic Regression improving from 86.67% to 91.11%, but KNN and Random Forest dropping from 97.78% to 84.44% and 91.11% to 82.22%, respectively. These findings suggest that while feature selection enhances performance for some models, it can negatively impact others, particularly those reliant on a larger feature set. Future research should explore adaptive feature selection techniques to optimize model performance across diverse datasets.

Feature selection is referred to the process of obtaining a subset from an original feature set according to certain feature selection criterion, which selects the relevant features of the dataset. It plays a role in compressing the data processing scale, where the redundant and irrelevant features are removed. Feature selection techniques show that more information is not always good in machine learning applications. Apply different algorithms for the data at hand and with baseline classification performance values we can select a final feature selection algorithm. In this paper, we propose a hybrid classification model, which has correlation based filter feature selection algorithm and Machine learning as classifiers. The objective of this study is to select relevant features and analyze the outperform machine learning algorithms in order to train our model, predict and compare their classification performance. In this method, features are ordered according to their Absolute correlation value with respect to the class attribute. Then top K Features are selected from ordered list of features to form a reduced dataset. This proposed classifier model is applied to our smart meter datasets. To measure the performance of these selected features; seven benchmark classifier are used; Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbor (kNN), Naive Bayes (NB), Decision Tree (DT), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). This paper then analyzes the performance of all classifiers with feature selection in term of accuracy, sensitivity, F-Measure, Specificity, Precision, and MCC. From our experiment, we found that Random Forest classifier performed higher than other used classifiers.

Exploring sex classification from earprints - A comparison of supervised machine learning algorithms and conventional linear discriminant analysis.

Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques. Random forest kerb feature selection (RFKFS) selects only 17 features from 754 attributes. The proposed technique uses validation metrics to assess the performance of ML models. The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56% and a precision of 88.02%, a sensitivity of 98.26%, a specificity of 96.06%. The respective validation score has an Non polynomial vector (NPV) of 99.47%, a Geometric Mean (GM) of 97.15%, a Youden’s index (YI) of 94.32%, and a Matthews’s correlation method (MCC) 90.84%. The proposed model is also more robust than other models. It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.

Using speech data, it is difficult to learn through machine learning how to diagnose Parkinson's disease (PD) and evaluate the effects of treatment. For this issue, the article has developed a three-stage PD discovery method. The base classifiers used in the initial stage are logistic regression (LR), K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVC), and decision tree (DT). The second stage, or stack model, is a meta-model that combines all of the classifiers mentioned earlier. The third stage ensemble model consists of Bagging, AdaBoost, Random Forest (RF), and Gradient Boosting (GBC) components. The RF and GBC classifiers are utilized to estimate the most important features from the PD dataset. The models' validation has been evaluated using the confusion matrix and validation metrics like precision, recall, and F1 score. Out of all the ensemble models, the GBC—the third model—had the highest accuracy with testing data—97.43%. KNN from the base model and stacking from the meta-model, on the other hand, had the highest accuracy, with 94.87% each. Out of all the models mentioned in this manuscript, the GBC is the only ensemble model classifier with the highest accuracy. The proposed classifier appears to be an extremely useful model for the discovery of Parkinson's disease, as demonstrated by the exploratory findings and factual analyses.

The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

Cardiovascular disease remains the most significant contributor to global mortality, highlighting the importance of early and precise risk assessment within preventive healthcare frameworks. Alongside the rapid growth of clinical data availability, machine learning approaches have increasingly been adopted to assist medical decision-making, particularly for interpreting complex and high-dimensional health information. This research investigates the predictive capability of six supervised machine learning models in determining the likelihood of cardiovascular disease incidence: Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosting. The Cleveland Heart Disease dataset from the UCI Machine Learning Repository served as the study's foundation. It includes 303 patient samples with a total of 76 recorded attributes. From this dataset, 14 clinically significant variables frequently reported in previous studies were selected for analysis. Considering the relatively small dataset size and the possibility of redundant or low-impact features, a feature selection approach was implemented to improve model robustness, minimize overfitting, and enhance interpretability. The data preparation process involved cleaning, normalization, feature selection, and division into datasets for testing and training. Metrics like accuracy, precision, recall, and F1-score were used to evaluate the model. The results of the experiment show that Random Forest and Logistic Regression models produced the highest predictive performance, followed by k-Nearest Neighbours and Support Vector Machine. These results indicate that supervised machine learning techniques, when supported by appropriate feature selection methods, are effective as decision-support tools for the early detection of cardiovascular disease.

Depression is an extremely serious illness of humans which causes constant mood swings and feelings of sadness. Nowadays, it is considered to be a deadly disorder in the world. At present, everyone from young to old is suffering from depression but most of them do not have the right idea about their mental state. Everyone needs to have a proper idea about their mental state. We will detect depression through a machine learning-based detection approach. Talking with psychologists and depressed people, we find some factors that are related to becoming depressed, and depending on those factors, information is collected from both depressed and non-depressed people. After applying preprocessing techniques, a processed dataset was created finally. Then, feature selection techniques were used. We applied eight machine learning algorithms and two feature selection methods to our dataset. We used k-nearest neighbor (k-NN), decision tree (DT), linear discriminant analysis (LDA), adaptive boosting (AB), support vector machine (SVM), naive Bayes (NB), random forest (RF), and logistic regression (LR) classifier. In our work, the RF classifier gave the best performance based on accuracy and the accuracy of the RF classifier was 96.00%.KeywordsMachine learningFeature selectionRandom forestSelectKBestDetectionDepression

Machine learning (ML) models are cost-effective tools that can establish a prognosis by considering clinical, genomic, and histological data from breast cancer patients. In this study, we evaluate the performance of 12 ML models in predicting the overall survival of breast cancer patients from “The Cancer Genome Atlas-Breast Cancer” database at 60 months of follow-up. The models evaluated were Logistic Regression (LR), Ridge Classifier (RC), Least Absolute Shrinkage and Selection Operator (LASSO), K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Decision Tree (DT), Multilayer Perceptron (MP), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). The most accurate models were NB (86.76%) and SGD (85.29%). The models with the highest sensitivity were SGD (93.75%), MP (91.67%), and NB (89.89%), while the models with the highest specificity were KNN (95.0%), DT (90.0%), and LASSO (85.0%). The models with the highest area under the ROC curve were LDA (90.83%), LASSO (90.1%), and LR (89.79%). The most relevant variables were “Previous Diagnosis of Cancer,” “Presence of Tumor,” “Ancillary Therapy,” and “Histology.” Our findings demonstrate that AI tools for predicting patient outcomes are a viable option when expensive prognostic genomic tools are not available. According to our results, NB and LASSO show promising performance in predicting the prognosis of breast cancer patients. Further studies with data from developing countries are needed to improve the performance of these tools.

BackgroundAcute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis.MethodsData of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model.ResultsA total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models.ConclusionThe ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

Feature selection plays a critical role in improving the efficiency, accuracy, and interpretability of machine learning models, particularly when dealing with high-dimensional datasets. Among various approaches, wrapper-based feature selection methods are known for their ability to capture feature interactions by directly optimizing model performance. This study presents a comprehensive comparative analysis of six wrapper feature selection techniques—Recursive Feature Elimination (RFE), Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE)—in conjunction with five widely used classification algorithms: Decision Tree, K-Nearest Neighbour, Random Forest, Logistic Regression, and Support Vector Machine. Experiments are conducted on an educational dataset comprising 395 student records with 30 attributes obtained from the UCI repository, using different feature subset sizes (all features, top 20, top 15, and top 10). Model performance is evaluated using accuracy, precision, recall, F1-score, and AUC. The results demonstrate that wrapper methods significantly enhance classification performance while reducing dimensionality, with GA and RFE consistently emerging as the most effective techniques across multiple classifiers. DE also shows strong performance, particularly with Logistic Regression and Random Forest, whereas PSO generally underperforms in terms of AUC. Furthermore, reducing the feature set does not adversely affect predictive accuracy and, in several cases, leads to improved generalization. The findings confirm the effectiveness of wrapper methods for educational data mining and provide practical insights for selecting optimal feature–classifier combinations.

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-based classifiers are applied to such data sets for risk stratification, leads to lower performance. Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk stratification accuracy. This ML-based risk stratification is designed, optimized and evaluated, where: (i) the features are extracted and optimized from the six feature selection techniques (random forest, logistic regression, mutual information, principal component analysis, analysis of variance, and Fisher discriminant ratio) and combined with ten different types of classifiers (linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, Gaussian process classification, support vector machine, artificial neural network, Adaboost, logistic regression, decision tree, and random forest) under the hypothesis that both missing values and outliers when replaced by computed medians will improve the risk stratification accuracy. Pima Indian diabetic dataset (768 patients: 268 diabetic and 500 controls) was used. Our results demonstrate that on replacing the missing values and outliers by group median and median values, respectively and further using the combination of random forest feature selection and random forest classification technique yields an accuracy, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve as: 92.26%, 95.96%, 79.72%, 91.14%, 91.20%, and 0.93, respectively. This is an improvement of 10% over previously developed techniques published in literature. The system was validated for its stability and reliability. RF-based model showed the best performance when outliers are replaced by median values.

Given the demand for developing the efficient Machine Learning (ML) classification models for healthcare data, and the potentiality of Bio-Inspired Optimization (BIO) algorithms to tackle the problem of high dimensional data, we investigate the range of ML classification models trained with the optimal subset of features of PD data set for efficient PD classification. We used two BIO algorithms, Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO), to determine the optimal subset of features of PD data set. The data set chosen for investigation comprises 756 observations (rows or records) taken over 755 attributes (columns or dimensions or features) from 252 PD patients. We employed MaxAbsolute feature scaling method to normalize the data and one hold cross-validation method to avoid biased results. Accordingly, the data is split in to training and testing set in the ratio of 70% and 30%. Subsequently, we employed GA and BPSO algorithms separately on 11 ML classifiers (Logistic Regression (LR), linear Support Vector Machine (lSVM), radial basis function Support Vector Machine (rSVM), Gaussian Naïve Bayes (GNB), Gaussian Process Classifier (GPC), k-Nearest Neighbor (kNN), Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Ada Boost (AB) and Quadratic Discriminant Analysis (QDA)), to determine the optimal subset of features (reduction of dimensionality) contributing to the highest classification accuracy. Among all the bio-inspired ML classifiers employed: GA-inspired MLP produced the maximum dimensionality reduction of 52.32% by selecting only 359 features and delivering 85.1% of the classification accuracy; GA-inspired AB delivered the maximum classification accuracy of 90.7% producing the dimensionality reduction of 41.43% by selecting only 441 features; And, BPSO-inspired GNB produced the maximum dimensionality reduction of 47.14% by selecting 396 features and delivering the classification accuracy of 79.3%; BPSOMLP delivered the maximum classification accuracy of 89% and produced 46.48% of the dimensionality reduction by selecting only 403 features.