Analysis and Prediction of Cardio Vascular Disease using Machine Learning Classifiers

Kumar, N. Komal; Sindhu, G.Sarika; Prashanthi, D.Krishna; Sulthana, A.Shaeen

doi:10.1109/icaccs48705.2020.9074183

Cited by 78 publications

(35 citation statements)

References 28 publications

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“…In research [76], the decision tree's accuracy is better than the random forest, around 1%. Nevertheless, in [86], the random forest's accuracy exceeds around 12% than the decision tree. Researchers in [73] and [80] used an ensemble model, where the accuracy was 98.50% and 90%.…”

Section: Discussionmentioning

confidence: 96%

“…In 2020, N. Komal Kumar and et al [86] suggested a model to predicate Cardio Vascular Disease (CVD) disease depend on the machine learning ML classification algorithms like the random forest, decision tree, logistic regression, SVM, and K.N.N. According to the obtained result, the random forest classifier algorithm has higher accuracy than the other classifier algorithm.…”

Section: H Deep Learningmentioning

confidence: 99%

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The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

157

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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Section: Discussionmentioning

confidence: 96%

Section: H Deep Learningmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

157

View full text Add to dashboard Cite

show abstract

“…Kumar et al [17] calculated various performance metrics like Accuracy, AUC ROC score, and execution time of various classifiers such as RF, DT, LR, SVM, and k-NN. It utilizes a heart disease dataset from the UCI repository and was carried out on Jupyter (Python).…”

Section: Related Workmentioning

confidence: 99%

“…An experiment was carried out on R Studio and the result concluded that HRFLM produced better accuracy (88.47%) than other classifiers. [6], [17], [19], [7]- [11], [13], [14], [16] 11 NB [7], [8], [10], [11], [13], [14], [16], [18], [20] 9…”

Section: Related Workmentioning

confidence: 99%

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Analyzing Predictive Algorithms in Data Mining for Cardiovascular Disease using WEKA Tool

Aman¹,

Chhillar²

2021

IJACSA

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Cardiovascular Disease (CVD) is the foremost cause of death worldwide that generates a high percentage of Electronic Health Records (EHRs). Analyzing these complex patterns from EHRs is a tedious process. To address this problem, Medical Institutions requires effective Predictive Algorithms for the Prognosis and Diagnosis of the Patients. Under this work, the current state-of-the-art studied to identify leading Predictive Algorithms. Further, these algorithms namely Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), AdaBoost and k-Nearest Neighbors (k-NN) analyzed against the two datasets on opensource WEKA software. This work used two similar structured datasets i.e., Statlog Dataset and Cleveland Dataset. For Pre-Processing of Datasets, The missing values were replaced with the Mean value and later 10 Fold Cross-Validation was utilized for the evaluation. The result of the performance analysis showed that SVM outperforms other algorithms against both datasets. SVM showed an accuracy of 84.156% against the Cleveland dataset and 84.074% against the Statlog dataset. LR showed a ROC Area of 0.9 against both datasets. The findings of the work will help Health Institutions to understand the importance and usage of Predictive Algorithms for the automatic prediction of CVD based on the symptoms.

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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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Analysis and Prediction of Cardio Vascular Disease using Machine Learning Classifiers

Cited by 78 publications

References 28 publications

The Role of Machine Learning Algorithms for Diagnosing Diseases

The Role of Machine Learning Algorithms for Diagnosing Diseases

Analyzing Predictive Algorithms in Data Mining for Cardiovascular Disease using WEKA Tool

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

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