ArticleComputational intelligence and neuroscience2022
AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- GBC-AST: a cluster-based oversampling method for heart failure prediction in imbalanced medical data sets.BMC medical informatics and decision making · 2026Article
- Assessment of quantum chemical predictors for anti-colorectal cancer agents using QSAR modeling.Journal of computer-aided molecular design · 2026Article
- Efficacy analysis and survival prediction of unique chemotherapy regimens for osteosarcoma in China.Frontiers in physiology · 2026Article
- Boosting pre-trained model with silica nanoparticles cellular toxicity prediction.Scientific reports · 2025Article
- Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households.BMC health services research · 2025Article
- Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and SHAP analysis: a retrospective cohort study.BMC geriatrics · 2025Article
- An interpretability heart disease prediction model based on stacking ensemble with SHAP.Frontiers in molecular biosciences · 2025Article
- Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology.Diagnostics (Basel, Switzerland) · 2024Article
- Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.The international journal of cardiovascular imaging · 2024Article
- Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images.Scientific reports · 2024Article
- Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations.Translational psychiatry · 2024Article
- Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron.Frontiers in artificial intelligence · 2024Article
- Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning.Sensors (Basel, Switzerland) · 2023Article
- Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach.Healthcare informatics research · 2023Article
- A Novel Blunge Calibration Intelligent Feature Classification Model for the Prediction of Hypothyroid Disease.Sensors (Basel, Switzerland) · 2023Article
- Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images.BioMed research international · 2023Article
- Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.CNS neuroscience & therapeutics · 2023Article
- Using Decision Tree Classification and AdaBoost Classification to Build the Abnormal Data Monitoring System of Financial Accounting in Colleges and Universities.Computational intelligence and neuroscience · 2022Article
- Efficient Hybrid Blind Watermarking in DWT-DCT-SVD with Dual Biometric Features for Images.Contrast media & molecular imaging · 2022Article
- Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor Using MRI Images.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a result of technology improvements, various features have been collected for heart disease diagnosis. Large data sets have several drawbacks, including limited storage capacity and long access and processing times. For medical therapy, early diagnosis of heart problems is crucial. Disease of heart is a devastating human disease that is quickly increasing in developed and also developing countries, resulting in death. In this type of disease, the heart normally fails to provide enough blood to different body parts in order to allow them to perform their regular functions. Early, as well as, proper diagnosis of this condition is very critical for averting further damage and also to save patients' lives. In this work, machine learning (ML) is utilized to find out whether a person has cardiac disease or not. Both the types of ensemble classifiers, namely, homogeneous as well as heterogeneous classifiers (formed by combining two separate classifiers), have been implemented in this work. The data mining preprocessing using Synthetic Minority Oversampling Technique (SMOTE) has been employed to cope with the imbalance problem of the class as well as noise. The proposed work has two steps. SMOTE is used in the initial phase to reduce the impact of data imbalance and the second phase is classifying data using Naive Bayes (NB), decision tree (DT) algorithms, and their ensembles. The experimental results demonstrate that the AdaBoost-Random Forest classifier provides 95.47% accuracy in the early detection of heart disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.