CML-Cardio: a cascade machine learning model to predict cardiovascular disease risk as a primary prevention strategy

Oliveira, Bruno Alberto Soares; Castro, Giulia Zanon de; Ferreira, Giovanna Luiza Medina; Guimarães, Frederico Gadelha

doi:10.1007/s11517-022-02757-z

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…Each tree is composed of nodes and branches. Each node represents an attribute in a group that needs to be categorized, and each branch represents a value that the node may accept 22 . The internal nodes are where the characteristics are kept, and the branches are where the outcomes of each test on each node are displayed.…”

Section: Methodsmentioning

confidence: 99%

“…Each node represents an attribute in a group that needs to be categorized, and each branch represents a value that the node may accept. 22 The internal nodes are where the characteristics are kept, and the branches are where the outcomes of each test on each node are displayed. DT is frequently used for categorization or parameter setting and is relatively simple to create since it does not require a lot of specialized expertise.…”

Section: Methodsmentioning

confidence: 99%

“…Because the KNN method only retains the training data without instantly learning from it, it is also referred to as the lazy learner algorithm. 22 When it receives new data to classify, it operates on the existing data and groups it based on similarity. KNN is a nonparametric technique for neighborhood discovery, output computation, and related data assessment.…”

Section: Methodsmentioning

confidence: 99%

“…k-nearest neighbor (KNN)A popular supervised learning technique for a variety of classification problems is KNN. Because the KNN method only retains the training data without instantly learning from it, it is also referred to as the lazy learner algorithm 22. When it receives new data to classify, it operates on the existing data and groups it based on similarity.…”

mentioning

confidence: 99%

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CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach

Sinha,

Narula,

Pandey

et al. 2024

Health Science Reports

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Background and AimsDiabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature‐fusion strategy employing supervised learning classifiers.MethodsPreprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics.ResultsBased on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes.ConclusionThe recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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