Prediction of Coronary Heart Disease using Supervised Machine Learning Algorithms

Krishnani, Divya; Kumari, Anjali; Dewangan, Akash; Singh, Aditya; Naik, Nenavath Srinivas

doi:10.1109/tencon.2019.8929434

Cited by 69 publications

(22 citation statements)

References 6 publications

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“…It may be noticed from Table 8 that the accuracy achieved by Divya et al [28] is 96.8% for Framingham dataset whereas, the accuracy achieved by our proposed MaLCaDD framework is 99.1 %. Moreover, much reduced number of features (i.e.…”

Section: Comparative Analysis and Discussionmentioning

confidence: 87%

“…Afterward, an existing ARM tool was applied for the generation of CARS for the prediction of chronic disease. Divya et al [28] propounded a solution for the automatic prediction of heart diseases. Firstly, machine learning algorithms were used, thereafter; the ensemble method was created for the final cross-validation results using machine learning and ensemble method.…”

Section: A Literature Reviewmentioning

confidence: 99%

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An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

et al. 2021

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Cardiovascular diseases are considered as the most life-threatening syndromes with the highest mortality rate globally. Over a period of time, they have become very common and are now overstretching the healthcare systems of countries. The major factors of cardiovascular diseases are high blood pressure, family history, stress, age, gender, cholesterol, Body Mass Index (BMI), and unhealthy lifestyle. Based on these factors, researchers have proposed various approaches for early diagnosis. However, the accuracy of proposed techniques and approaches needs certain improvements due to the inherent criticality and life threatening risks of cardiovascular diseases. In this article, a MaLCaDD (Machine Learning based Cardiovascular Disease Diagnosis) framework is proposed for the effective prediction of cardiovascular diseases with high precision. Particularly, the framework first deals with the missing values (via mean replacement technique) and data imbalance (via Synthetic Minority Over-sampling Technique -SMOTE). Subsequently, Feature Importance technique is utilized for feature selection. Finally, an ensemble of Logistic Regression and K-Nearest Neighbor (KNN) classifiers is proposed for prediction with higher accuracy. The validation of framework is performed through three benchmark datasets (i.e. Framingham, Heart Disease and Cleveland) and the accuracies of 99.1%, 98% and 95.5 % are achieved respectively. Finally, the comparative analysis prove that MaLCaDD predictions are more accurate (with reduced set of features) as compared to the existing state-of-the-art approaches. Therefore, MaLCaDD is highly reliable and can be applied in real environment for the early diagnosis of cardiovascular diseases.

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Section: Comparative Analysis and Discussionmentioning

confidence: 87%

Section: A Literature Reviewmentioning

confidence: 99%

An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

et al. 2021

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“…Clinicians and physicians can take advantage of machine learning for clinical feature ranking and unveil hidden and non-obvious correlations and relationships between patients' data. Several supervised machine learning classifiers were used for this purpose and have achieved success in this regard such as logistic regression, SVM, deep learning, KNN, decision tree [3,[13][14][15]. However, most of the machine learning models designed for the prediction of CHD have achieved modest accuracy [16], More recent models show some improvements but only in the prediction accuracy though [17,18].…”

Section: Introductionmentioning

confidence: 99%

Exploring Machine Learning Techniques for Coronary Heart Disease Prediction

Khdair¹,

Dasari²

2021

IJACSA

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Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance.

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“…In recent years, the amount of data in the healthcare centers is large. Machine learning algorithms are widely applied to the larger medical datasets to discover interesting pattern and knowledge from the dataset and automate disease diagnosis with predictive model [4][5][6][7]. One of the approach for discovering important pattern from a medical dataset is exploring the relationships among dataset features.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

Assegie

Sushma

Bhavya³

et al. 2021

SN COMPUT. SCI.

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Heart disease is one of the causes for death throughout the world. Heart disease cannot be easily identified by the medical experts and practitioners as the detection of heart disease requires expertise and experience. Hence, developing better performing models for heart disease detection using machine-learning algorithms is crucial for detecting heart disease in an early stage. However, employing machine learning algorithm involves determining the relationship between the heart failure dataset features. In this study, correlation analysis is employed to identify the relationship among the heart failure dataset features and a predictive model for heart failure detection is developed with K-nearest neighbor (KNN). Pearson correlation is employed to identify the relationship between the features in the heart failure dataset and the effect of strong correlation to the target feature on the performance of K-nearest neighbor (KNN) model is analyzed. The experimental result shows that highly correlated feature significantly affected the performance of K-nearest neighbor (KNN) for heart failure detection. Finally, the performance of KNN is evaluated and result reveals that the model has acceptable level of performance with highest accuracy of 97.07% on heart failure prediction.

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Prediction of Coronary Heart Disease using Supervised Machine Learning Algorithms

Cited by 69 publications

References 6 publications

An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

Exploring Machine Learning Techniques for Coronary Heart Disease Prediction

Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

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