Fault Prediction and Classification in Power Distribution Transformer Using Machine Learning

Dengue fever is a worldwide issue, especially in Yemen. Although early detection is critical to reducing dengue disease deaths, accurate dengue diagnosis requires a long time due to the numerous clinical examinations. Thus, this issue necessitates the development of a new diagnostic schema. The objective of this work is to develop a diagnostic model for the earlier diagnosis of dengue disease using Efficient Machine Learning Techniques (EMLT). This paper proposed prediction models for dengue disease based on EMLT. Five different efficient machine learning models, including K-Nearest Neighbor (KNN), Gradient Boosting Classifier (GBC), Extra Tree Classifier (ETC), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM). All classifiers are trained and tested on the dataset using 10-Fold Cross-Validation and Holdout Cross-Validation approaches. On a test set, all models were evaluated using different metrics: accuracy, F1-sore, Recall, Precision, AUC, and operating time. Based on the findings, the ETC model achieved the highest accuracy in Hold-out and 10-fold cross-validation, with 99.12 % and 99.03 %, respectively. In the Holdout cross-validation approach, we conclude that the best classifier with high accuracy is ETC, which achieved 99.12 %. Finally, the experimental results indicate that classifier performance in holdout cross-validation outperforms 10-fold cross-validation. Accordingly, the proposed dengue prediction system demonstrates its efficacy and effectiveness in assisting doctors in accurately predicting dengue disease.

In the field of data-driven healthcare systems, wireless-based human activity monitoring in an indoor environment is getting popular. Localization of human activities can help patients to get remote healthcare. Wearable devices are employed in modern healthcare monitoring systems, despite the fact that they can be inconvenient and expensive. Several studies have shown that analyzing variations in Channel State Information (CSI) can be used to identify activities in an indoor environment and monitor health utilizing radio frequencies. The results of an experiment using Software Defined Radio (SDR) to identify and localize human activities while a human subject performed six different classes of activities (no-activity, leaning forward, sitting, standing, walking-Rx, walking-Tx). The CSI extracted from the communication channel between two Universal Software Radio Peripherals (USRP) devices communicating and operating as SDRs was used to collect a total of 2100 sample data. The Extra Tree (ET) classifier outperformed the prior benchmark approaches in localizing six activities across the 21 classes. The proposed ET classifier can identify if the room is occupied and a human subject’s walking directions in two separate zones, as well as localize six distinct activities inside the room with an accuracy of 91% and 100% respectively.

Coronary Artery Disease (CAD) is a cardiovascular disease that has the highest mortality rate. The non-invasive method for diagnosing CAD is coronary angiography which is expensive and exposes the patient to radiation. The present scenario for diagnosing CAD has motivated researchers to analyze and diagnose the disease using Machine Learning (ML) algorithms. In this work, the efficacy of various ML algorithms used to diagnose CAD disease is presented and compared. The proposed methodology aims to train the dataset using algorithms namely: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Naïve Bayes, Decision Tree, Extreme Gradient Boosting, Random Forest, Extra Tree Classifier, and Light Gradient Boosting machine, to achieve accuracy for diagnosing the CAD. The experimental results are validated in the simulation environment, and the conclusions were drawn from the performance indices, i.e., Accuracy, Sensitivity, Specificity, Precision, F1-Score, and Binary cross-entropy cost function. The meta-analysis shows that Light Gradient Machine achieves an accuracy of 93.36%, which is the highest among other ML algorithms.

This research paper focuses on creating a machine learning algorithm-powered artificial intelligence system for heart disease detection. The main goal is to use different machine learning models to predict heart disease in patients. Adaptive Boosting (Adaboost), Classification and Regression Tree (CART), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Multilayer Perceptron (MLP), Extra Tree Classifier (ET), Extreme Gradient Boosting (XGB), Sustainable Development Goals (SDG), and Random Forest (RF) are some of these models. This research article attempts to determine the best algorithm for heart disease prediction through a comparison analysis. The results show that XGB outperforms other algorithms in terms of performance, achieving the maximum accuracy of 97.91%.

In recent years, there has been an increase in thyroid disorders. Early thyroid illness identification is a crucial endeavor because of its significance in metabolism. Although there are numerous studies on the identification of whether thyroid illness leads to cancer or not, in order to properly address and handle this condition, a timely and accurate medical diagnosis is crucial. Thyroid cancer detection and diagnosis could be enhanced by algorithms that utilize machine learning, which have achieved considerable amounts of interest especially in the medical field. This paper describes how machine learning techniques are being used to diagnose thyroid cancer. K-Nearest Neighbor, Decision Tree, Random orest, Ada-Boost with decision tree classifier, Gradient Boosting Classifier, Stochastic Gradient Boosting classifier, Extended Gradient Boosting Classifier, Extended Gradient Boosting Classifier with hyper parameter tuning, Extra Tree Classifier, Light GBM Classifier, Voting Classifier are among the machine learning approaches that are used and evaluated to assess how well they diagnose thyroid Cancer. The study evaluates the methods precision, recall, F1-score, and Accuracy. From all the above machine learning models Random Forest, Ada Boost, Extended gradient boosting classifier, Extra Tree Classifier, Light Gradient Boosting Machine outperforms the other models.

An optimized XGBoost based diagnostic system for effective prediction of heart disease

Cardiovascular diseases (CVDs) have become increasingly prevalent and severe worldwide, necessitating a transition to more advanced and precise predictive models. Traditional approaches to the prognosis of coronary heart disease often lead to higher morbidity and mortality due to delayed early detection and inadequate individualized analysis. Machine learning (ML) techniques offer a groundbreaking solution, capable of analyzing vast amounts of patient data to provide early and accurate predictions of cardiac diseases. This shift holds great promise across various domains, including heart imaging, cardiothoracic surgery, cardiovascular anesthesiology, coronary critical care, pediatric cardiovascular disease, predictive cardiology, and interventional cardiology. In this comprehensive review, we evaluate several ML techniques, specifically focusing on classification and prediction algorithms, to assess their effectiveness in predicting cardiovascular diseases. We examine a range of algorithms such as K-Nearest Neighbors (K-NN), Decision Trees (DT), Support Vector Machines (SVM), Random Forests, Logistic Regression, XGBoost, Extra Trees Classifier, Gaussian Naive Bayes, and Artificial Neural Networks. By comparing these models, we aim to identify the most reliable and effective predictive tools for cardiovascular diseases. Notably, algorithms like Decision Trees and Extra Trees Classifiers have demonstrated remarkable performance, with recorded accuracies of 93.19% and 98.15%, respectively. This study underscores the importance of implementing machine learning approaches to enhance the accuracy and predictive capabilities in cardiac care. By leveraging these models, we can significantly improve early diagnosis, leading to better patient outcomes across cardiology specialties.

Background Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques. Method In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions. Results The nonparametric Friedman – Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.

Retrospective cohort study. This study aimed to develop machine-learning algorithms to predict ambulation outcomes following surgery for spinal metastasis. Postoperative ambulation status following spinal metastasis surgery is currently difficult to predict. The improved ability to predict this important postoperative outcome would facilitate management decision-making and help in determining realistic treatment goals. This retrospective study included patients who underwent spinal metastasis at a university-based medical center in Thailand between January 2009 and November 2021. Collected data included preoperative parameters and ambulatory status 90 and 180 days following surgery. Thirteen machine-learning algorithms, namely, artificial neural network, logistic regression, CatBoost classifier, linear discriminant analysis, extreme gradient boosting, extra trees classifier, random forest classifier, gradient boosting classifier, light gradient boosting machine, naïve Bayes, K-neighbor classifier, Ada boost classifier, and decision tree classifier were developed to predict ambulatory status 90 and 180 days following surgery. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1-score. In total, 167 patients were enrolled. The number of patients classified as ambulatory 90 and 180 days following surgery was 140 (81.9%) and 137 (82.0%), respectively. The extreme gradient boosting algorithm was found to most accurately predict 180-day ambulatory outcome (AUC, 0.85; F1-score, 0.90), and the decision tree algorithm most accurately predicted 90-day ambulatory outcome (AUC, 0.94; F1-score, 0.88). Machine-learning algorithms were effective in predicting ambulatory status following surgery for spinal metastasis. Based on our data, the extreme gradient boosting and decision tree best predicted postoperative ambulatory status 180 and 90 days after spinal metastasis surgery, respectively.

In this digital world, technology is changing every second. For this reason, tech-based educational departments should be aware of their strong and weak areas. By doing so, they can analyze their performance and emphasis the respected fields to prepare their students for the challenging job market and higher studies. In this research, a novel machine learning model is proposed to predict the performance of computer science department students by analyzing their academic statements. The academic areas of the departments are divided into fifteen fields, each of which is categorized as Excellent, Very Good, Good, Average, and Bad. One thousand students’ academic statements were used as the dataset. The prediction outputs are labeled into multiple categories, with multiclass data in each. Different models are developed using multiclass-multioutput classification algorithms like Decision Tree (Tree), Extra Tree (Tree), Extra Trees (Ensemble), K-Neighbors (Neighbors), Radius Neighbors (Neighbors), and Random Forest (Ensemble) Classifier. From the developed models, the model that used "Random Forest Classifier (Ensemble)" provided the best results, and the accuracy was almost 94%. Finally, a comparison of our research with previous and existing different developed models is presented to show the novelty of this research.

In recent studies, researchers have focused on using various modalities to recognize emotions for different applications. A major challenge is identifying emotions correctly with only electrocardiograms (ECG) as the modality. The main objective is to reduce costs by using single-modality ECG signals to predict human emotional states. This paper presents an emotion recognition approach utilizing the heart rate variability features obtained from ECG with feature selection techniques (exhaustive feature selection (EFS) and Pearson’s correlation) to train the classification models. Seven machine learning (ML) models: multi-layer perceptrons (MLP), Support Vector Machine (SVM), Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), Logistic Regression, Adaboost and Extra Tree classifier are used to classify emotional state. Two public datasets, DREAMER and SWELL are used for evaluation. The results show that no particular ML works best for all data. For DREAMER with EFS, the best models to predict valence, arousal, and dominance are Extra Tree (74.6%), MLP and DT (74.6%), and GBDT and DT (69.8%), respectively. Extra tree with Pearson’s correlation are the best method for the ECG SWELL dataset and provide 100% accuracy. The usage of Extra tree classifier and feature selection technique contributes to the improvement of the model accuracy. Moreover, the Friedman test proved that ET is as good as other classification models for predicting human emotional state and ranks highest. Doi: 10.28991/ESJ-2023-07-01-011 Full Text: PDF

Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In this work, Parkinson’s disease is classified with the help of human voice signals. Six different machine learning (ML) algorithms are used in the classification: Stochastic Gradient Descent (SGD) Classifier, Extreme Gradient Boosting (XGB) Classifier, Logistic Regression Classifier, Random Forest Classifier, K-Nearest Neighbour (KNN) Classifier, and Decision Tree (DT) Classifier. This research aims to classify Parkinson’s disease using human voice signals and extract essential features to reduce the complexity of the dataset. Then, human voice signals are analyzed to check the voice intensity and spectrum for PD patients. Then, machine learning classifiers are applied to classify the PD patients based on the extracted features. The results show that SGD-Classifier has 91% accuracy, XGB-Classifier has 95% accuracy, Logistic Regression has 91% accuracy, Random Forest shows 97% accuracy, KNN shows 95% accuracy, and Decision Tree has 95% accuracy. Hence, Random Forest has the highest accuracy. The disease can be studied more by looking for more characteristics of PD patients to enhance its proper use in the medical field.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental condition that affects millions of individuals globally. This study leverages the publicly available UCI Machine Learning ASD Screening Dataset, which contains behavioral questionnaire responses from children aged 4-11 years, to develop and compare multiple machine learning (ML) models for early ASD diagnosis. This study applies SHAP (SHapley Additive exPlanations) to interpret and analyze the feature importance of various attributes in the dataset. Advanced ML models like Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extra Trees Classifier (ETC), Support Vector Machines (SVM), and Multilayer Perceptrons (MLPs) were implemented to evaluate and compare their performance in predicting early ASD. The developed ASD model underwent a rigorous evaluation through comprehensive performance metrics, including accuracy, precision, recall, F1-score, specificity, and Matthews correlation coefficient. Ranking-based metrics, such as area under the receiver operating characteristic curve (AUROC), provided additional insights into classification robustness, while 10-fold cross-validation ensured reliability and generalizability. Results indicate that XGBoost achieved the highest diagnostic performance (accuracy = 97.8%, AUROC = 0.99), followed closely by MLP, with RF and ETC offering balanced performance and interpretability. SHAP analysis identified key behavioral features influencing model predictions, enabling explainable AI-driven insights. These findings suggest that ML-based tools can support clinicians in identifying ASD earlier, allowing for timelier interventions in disability services, particularly in underserved populations. While results are promising, the use of secondary data and the lack of clinical validation remain limitations, highlighting the need for real-world pilot testing. These findings underscore the transformative potential of ML techniques in enhancing ASD diagnostics. Despite their promise, the results are constrained by reliance on secondary data and the absence of clinical validation, underscoring the need for real-world pilot testing. Nonetheless, incorporating these models into clinical practice could accelerate early detection, facilitate personalized interventions, and optimize resource allocation for improved ASD management outcomes.

In the real world, there is a significant presence of imbalanced data due to the fact that the classes that make up the datasets are not evenly distributed. Even when using methods that are traditionally used to achieve class balance, such as re-sampling & re-weighting, current deep learning still faces a significant obstacle because of the class imbalance. This study’s major objective is proposing a data augmentation technique to balance the data to improve the sample sizes for the minority classes. Python, a well-known programming language, & multiple methods of machine learning are being employed in the execution of this study. Classification models like Logistic Regression, Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra Trees Classifier, AdaBoost classifier, Gradient Boost classifier was used to implement this study. Precision, recall, & F-score were used to determine which model would be the most effective. According to the findings of this study's analysis, the Naive Bayes approach, which has a F1-Score of 95.85% & has Wn = 3, Cn = 3, & CWn =3 as its parameters, is the technique that yields the most accurate results.

Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-‘2011’, ‘Miraj-‘08’, and ‘Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.