Identifying the Main Risk Factors for Cardiovascular Diseases Prediction Using Machine Learning Algorithms

Guarneros-Nolasco, Luis Rolando; Cruz-Ramos, Nancy Aracely; Alor-Hernández, Giner; Rodríguez-Mazahua, Lisbeth; Sánchez-Cervantes, José Luis

doi:10.3390/math9202537

Cited by 26 publications

(9 citation statements)

References 37 publications

(45 reference statements)

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“…According to the study, the risk variables can be employed for follow-up in the early detection of CVDs, such as arrhythmia or tachycardia, and for prompt and effective treatment when required. The report suggests that other medical databases should be used to replicate the study and that mobile applications for heart disease monitoring should be created utilizing the discovered risk variables [23].…”

Section: Literature Reviewmentioning

confidence: 99%

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Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students

Musleh,

Alkhwaja,

Alkhwaja

et al. 2024

BDCC

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Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict CMD using various machine learning techniques. Support vector machines (SVMs), K-Nearest neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting are five robust classifiers that are compared in this study. A novel “risk level” feature, derived through fuzzy logic applied to the Conicity Index, as a novel feature, which was previously unused, is introduced to enhance the interpretability and discriminatory properties of the proposed models. As the Conicity Index scores indicate CMD risk, two separate models are developed to address each gender individually. The performance of the proposed models is assessed using two datasets obtained from 295 records of undergraduate students in Saudi Arabia. The dataset comprises 121 male and 174 female students with diverse risk levels. Notably, Logistic Regression emerges as the top performer among males, achieving an accuracy score of 91%, while Gradient Boosting lags with a score of 72%. Among females, both Support Vector Machine and Logistic Regression lead with an accuracy score of 87%, while Random Forest performs least optimally with a score of 80%.

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Section: Literature Reviewmentioning

confidence: 99%

“…However, as the dataset size increases, the computational complexity of KNN may also rise. Nonetheless, KNN proves to be highly effective in tasks such as anomaly detection, pattern identification, and recommendation systems, providing a straightforward and adaptable solution to machine learning challenges [23].…”

Section: K-nearest Neighborsmentioning

confidence: 99%

Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students

Musleh,

Alkhwaja,

Alkhwaja

et al. 2024

BDCC

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“…To maintain consistency across all datasets, we chose three as the maximum number of features for comparison. Other works related to disease prediction have presented a similar process [54][55].…”

Section: Most Important Attributesmentioning

confidence: 99%

Machine Learning Analysis of Factors Contributing to Diabetes Development

Edgar Ceh-Varela,

Maes,

Sarbagya Ratna Shakya

2024

Cloud Computing and Data Science

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Diabetes is a chronic condition that affects how the body processes blood sugar. Early diagnosis and management of diabetes are essential for preventing these complications. Machine Learning (ML) techniques offer an effective means to accurately diagnose diabetes by identifying key risk factors and developing predictive models. In this study, we assess the performance of 11 ML algorithms on four diabetes prediction datasets, considering the top 2, top 3, and all attributes. Through k-fold cross-validation, we ensure robust and generalizable results. We use a set of standard evaluation metrics such as accuracy, precision, recall, f1-score, and Receiver Operating Characteristic curve (ROC_AUC). Our analysis aims to determine the optimal number of features and assess how performance changes with feature additions. Notably, some ML classifiers achieve satisfactory classification and predictive abilities using only the top 2 or 3 features. Furthermore, varying dataset performances across algorithms highlight the need for assessing multiple models to identify the most suitable one. These findings enable the creation of dependable models that enhance patient outcomes by leveraging effective algorithms and pertinent features.

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“…Results suggest that the maximum accuracy of 90.789% is obtained using KNN. Similarly, study [ 20 ] used four different benchmark datasets to perform CVD prediction. The performance is analyzed using the top 2 and top 4 features/attributes from the datasets.…”

Section: Related Workmentioning

confidence: 99%

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

et al. 2022

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Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets.

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Identifying the Main Risk Factors for Cardiovascular Diseases Prediction Using Machine Learning Algorithms

Cited by 26 publications

References 37 publications

Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students

Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students

Machine Learning Analysis of Factors Contributing to Diabetes Development

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

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