A comparative analysis of boosting algorithms for chronic liver disease prediction

Healthcare Analytics, Год журнала: 2024, Номер 5, С. 100313 - 100313

Опубликована: Фев. 23, 2024

Chronic liver disease (CLD) is a major health concern for millions of people all over the globe. Early prediction and identification are critical taking appropriate action at earliest stages disease. Implementing machine learning methods in predicting CLD can greatly improve medical outcomes, reduce burden condition, promote proactive preventive healthcare practices those risk. However, traditional has some limitations which be mitigated through ensemble learning. Boosting most advantageous approach. This study aims to performance available boosting techniques prediction. Seven popular algorithms Gradient (GB), AdaBoost, LogitBoost, SGBoost, XGBoost, LightGBM, CatBoost, two publicly datasets (Liver patient dataset (LDPD) Indian (ILPD)) dissimilar size demography considered this study. The features ascertained by exploratory data analysis. Additionally, hyperparameter tuning, normalisation, upsampling used predictive analytics. proportional importance every feature contributing algorithm assessed. Each algorithm's on both assessed using k-fold cross-validation, twelve metrics, runtime. Among five algorithms, GB emerged as best overall performer datasets. It attained 98.80% 98.29% accuracy rates LDPD ILPD, respectively. also outperformed other regarding metrics except

Язык: Английский

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Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 22, 2025

Heart disease is one of the leading causes death worldwide. Predicting and detecting heart early crucial, as it allows medical professionals to take appropriate necessary actions at earlier stages. Healthcare can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed enhance prediction using stacking voting ensemble methods. Fifteen base models were trained on two different datasets. After evaluating various combinations, six pipelined develop employing a meta-model (stacking) majority vote (voting). The performance was compared that individual models. To ensure robustness evaluation, we conducted statistical analysis Friedman aligned ranks test Holm post-hoc pairwise comparisons. results indicated developed models, particularly stacking, consistently outperformed other achieving higher accuracy improved predictive outcomes. rigorous validation emphasised reliability proposed Furthermore, incorporated explainable AI (XAI) through SHAP interpret model predictions, providing transparency insight into how features influence prediction. These findings suggest combining predictions multiple or may serve valuable tool in clinical decision-making.

Язык: Английский

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A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques

Computation, Год журнала: 2024, Номер 12(1), С. 15 - 15

Опубликована: Янв. 16, 2024

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing need for accurate predictive models. The timely identification of individuals at risk developing cardiovascular ailments is paramount implementing preventive measures interventions. World Health Organization (WHO) reports that diseases, responsible an alarming 17.9 million annual fatalities, constitute a significant 31% mortality rate. intricate clinical landscape, characterized by inherent variability complex interplay factors, poses challenges accurately diagnosing severity cardiac conditions predicting their progression. Consequently, early emerges as pivotal factor successful treatment heart-related ailments. research presents comprehensive framework prediction leveraging advanced boosting techniques machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, Ada boost. Focusing “Early Heart Disease Prediction using Boosting Techniques”, this aims to contribute development robust models capable reliably forecasting health risks. Model performance rigorously assessed substantial dataset illnesses from UCI library. With 26 feature-based numerical categorical variables, encompasses 8763 samples collected globally. empirical findings highlight AdaBoost preeminent performer, achieving notable accuracy 95% excelling metrics such negative predicted value (0.83), false positive rate (0.04), (0.01). These results underscore AdaBoost’s superiority overall compared alternative algorithms, contributing valuable insights field prediction.

Язык: Английский

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Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches

BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)

Опубликована: Июнь 7, 2024

Liver disease causes two million deaths annually, accounting for 4% of all globally. Prediction or early detection the via machine learning algorithms on large clinical data have become promising and potentially powerful, but such methods often some limitations due to complexity data. In this regard, ensemble has shown results. There is an urgent need evaluate different then suggest a robust algorithm in liver prediction.

Язык: Английский

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Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence

PLoS ONE, Год журнала: 2024, Номер 19(11), С. e0307654 - e0307654

Опубликована: Ноя. 14, 2024

Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it critical component of sustainable practices. Decision tree-based ensemble learning techniques are particularly effective for this task due to their ability process complex datasets with high accuracy. Furthermore, incorporating explainable artificial intelligence into these predictions provides clarity interpretability, allowing managers homeowners make informed decisions that optimize usage reduce costs. This study comparatively analyzes decision tree–ensemble augmented transparency interpretability building forecasting. approach employs the University Residential Complex Appliances Energy Prediction datasets, data preprocessing, decision-tree bagging boosting methods. The superior model is evaluated using Shapley additive explanations method within framework, explaining influence input variables decision-making processes. analysis reveals significant temperature-humidity index wind chill temperature short-term load forecasting, transcending traditional parameters, such as temperature, humidity, speed. complete source code have been made available our GitHub repository at https://github.com/sodayeong purpose enhancing precision system thereby promoting enabling replication.

Язык: Английский

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ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach

Computer Methods and Programs in Biomedicine Update, Год журнала: 2025, Номер unknown, С. 100173 - 100173

Опубликована: Янв. 1, 2025

Язык: Английский

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A novel NEMONET framework for enhanced RCC detection and staging in CT images

Deleted Journal, Год журнала: 2025, Номер 28(1)

Опубликована: Янв. 15, 2025

This study introduces NemoNet, a novel deep-learning framework designed for the automated detection and staging of Renal Cell Carcinoma (RCC) in 3D CT images. Leveraging comprehensive HubMAP RCC dataset, NemoNet integrates encoder-decoder architecture with advanced radiomic feature analysis to enhance tumour segmentation accuracy. The model employs multi-objective loss function balance precision prediction, outperforming traditional architectures like U-Net ResNet. Evaluation metrics, including Dice Coefficient, sensitivity, specificity, indicate superior performance, achieving an accuracy 92% score 0.88. While demonstrates robust results, challenges remain handling variability imaging quality full interpretability. findings suggest that offers significant advancements staging, potential applications personalized oncology treatment planning.

Язык: Английский

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Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 23, 2025

Язык: Английский

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A Study of Possible AI Aversion in Healthcare Consumers

Опубликована: Март 6, 2025

Язык: Английский

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A Study of AI Application Through Integrated and Systematic Moral Cognitive Therapy in the Healthcare Sector

Опубликована: Март 6, 2025

Язык: Английский

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