Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107289 - 107289

Published: Aug. 1, 2023

Language: Английский

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Artificial intelligence for heart disease prediction and imputation of missing data in cardiovascular datasets

Cogent Engineering, Journal Year: 2024, Volume and Issue: 11(1)

Published: March 13, 2024

According to World Health Organization (WHO) data, cardiovascular diseases (CAD) continue take the lives of more than 17.9 million people worldwide each year. Heart attacks are considered a fatal disease in this category, especially for older adults, which highlights need employ artificial intelligence anticipate disease. This research faces many challenges, starting with data quality and availability, where AI models require large high-quality datasets training. Elderly populations exhibit various health conditions, lifestyle factors, genetic diversity. Creating that can accurately generalize across such diverse group be challenging. Two CAD were used study. Traditional machine learning (ML) techniques on these datasets, as well neural network method based extreme machines (ELM), provided varying percentages accuracy, time, average estimated error. The ELM algorithm outperformed all other algorithms by attaining best shortest execution lowest percentage Experimental results showed Extreme performed 200 hidden neurons, even proposed absence parts dataset, an accuracy 97.57–99.06%.

Language: Английский

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Hepatitis C prediction using SVM, logistic regression and decision tree

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 22(2), P. 926 - 936

Published: May 16, 2024

Hepatitis C is an infection of the liver brought on by HCV virus. In this condition, early diagnosis challenging because delayed onset symptoms. Predicting well enough can spare patients from permeant damage. The primary goal work to use several machine learning methods forecast disease based widely available and reasonably priced blood test data in order diagnose treat on. Three techniques support vector (SVM), logistic regression, decision tree, has been applied one dataset work. To find a suitable approach for illness prediction, confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), performances different strategies have assessed. SVM model's overall accuracy 0.92, highest among three models.

Language: Английский

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Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study

Journal of Cardiovascular Development and Disease, Journal Year: 2024, Volume and Issue: 11(7), P. 207 - 207

Published: July 1, 2024

Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke identifying key risk factors using data from Suita study, comprising 7389 participants 53 variables. Initially, unsupervised k-prototype clustering categorized into clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Boosted (LightGBM) were employed predict outcomes. incidence disparities among identified clusters method are substantial, according findings. Supervised learning, particularly RF, was preferable option because higher levels performance metrics. The Shapley Additive Explanations (SHAP) age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, glucose level as predictors stroke, aligning with findings approach high-risk groups. Additionally, previously unidentified such elbow joint thickness, fructosamine, hemoglobin, calcium demonstrate potential for prediction. In conclusion, facilitated accurate predictions highlighted biomarkers, offering data-driven framework assessment biomarker discovery.

Language: Английский

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Predicting stroke risk: An effective stroke prediction model based on neural networks

Journal of Neurorestoratology, Journal Year: 2024, Volume and Issue: unknown, P. 100156 - 100156

Published: Sept. 1, 2024

Language: Английский

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