An Electrocardiogram Signal Classification Using a Hybrid Machine Learning and Deep Learning Approach

Healthcare Analytics, Journal Year: 2024, Volume and Issue: 6, P. 100366 - 100366

Published: Oct. 9, 2024

Language: Английский

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An Enhanced Machine Learning Approach with Stacking Ensemble Learner for Accurate Liver Cancer Diagnosis Using Feature Selection and Gene Expression Data

Healthcare Analytics, Journal Year: 2024, Volume and Issue: unknown, P. 100373 - 100373

Published: Dec. 1, 2024

Language: Английский

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A Comparative Study on Machine Learning Classifiers for Cervical Cancer Prediction: A Predictive Analytic Approach

EAI Endorsed Transactions on Internet of Things, Journal Year: 2024, Volume and Issue: 11

Published: Nov. 19, 2024

INTRODUCTION: Cervical cancer is a significant global health concern, particularly in underdeveloped nations where preventive healthcare measures are limited. Early identification of the risks associated with cervical essential for both prevention and treatment. OBJECTIVES: In recent years, machine-learning algorithms have gained popularity as potential techniques determining person's risk developing based on demographic medical information. This study uses dataset that contains patient demographics, clinical history, results from diagnostic tests to examine how machine learning-based can be used predict cancer. METHODS: Various learning approaches create predictive systems, including Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), Nearest Centroid (NC), Multilayer Perceptron(MP), AdaBoost (AB). RESULTS: The prediction capability these models assessed using performance metrics such accuracy, sensitivity, specificity, f-measure, precision, area under receiver operating characteristic curve (AUC-ROC). Our show decision tree has highest f1-score (98.91%, 97.81%, 0.9889). Additionally, model was optimized by use hyperparameter tuning. After adjustment, (SVM) showed superior accuracy 99.64%, precision 99.26%, an F1-score 0.9963, thereby indicating its probability prediction. We also created web application estimate CONCLUSION: findings this highlight significance SVM demonstrate capabilities enhance accurate outcomes screening.

Language: Английский

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An investigation of ensemble learning techniques for obesity risk prediction using lifestyle data

Decision Analytics Journal, Journal Year: 2024, Volume and Issue: unknown, P. 100539 - 100539

Published: Dec. 1, 2024

Language: Английский

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