Methodologies and Techniques for Heart Disease Classification and Prediction

Sangle, Priyanka S.; Goudar, R. M.; Bhute, Avinash N.

doi:10.1109/icccnt49239.2020.9225673

Cited by 9 publications

(3 citation statements)

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“…Results concluded that AdaBoost gives almost the same accuracy (89%) at test sizes 40% and 10% of the model. Sangle et al [13] analyzed the theoretical aspect of different work in the field of ML and Deep Learning (DL) for the prediction of Cardiovascular Disease. They have studied the pros/cons of techniques like DT, k-NN, SVM, NB, ANN, and Ensemble Learning.…”

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%

“…DT [5], [6], [18], [19], [8], [10]- [14], [16], [17] 12 k-NN [5]- [10], [13], [14], [16], [17] 10 LR [8], [10], [17], [19] 4…”

Section: Related Workunclassified

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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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Section: Related Workmentioning

confidence: 99%