Heart Disease Prediction using Machine Learning Techniques

Shah, Devansh; Patel, Samir; Bharti, Santosh Kumar

doi:10.1007/s42979-020-00365-y

Cited by 401 publications

(130 citation statements)

References 11 publications

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“…Shah et al [40] proposed a system to study different conditions that can affect the heart and primary factors for the deaths. Different supervised machine learning algorithms were used such as Decision Tree (DT), Naïve Bayes (NB), RF, and KNN.…”

Section: Related Workmentioning

confidence: 99%

Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques

et al. 2021

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Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining transforms huge amounts of raw data generated by the health industry into useful information that can help in making informed decisions. Various studies proved that significant features play a key role in improving performance of machine learning models. This study analyzes the heart failure survivors from the dataset of 299 patients admitted in hospital. The aim is to find significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient's survivor prediction. To predict patient's survival, this study applies nine classification models: Decision Tree (DT), Adaptive boosting classifier (AdaBoost), Logistic Regression (LR), Stochastic Gradient classifier (SGD), Random Forest (RF), Gradient Boosting classifier (GBM), Extra Tree Classifier (ETC), Gaussian Naive Bayes classifier (G-NB) and Support Vector Machine (SVM). The imbalance class problem is handled by Synthetic Minority Oversampling Technique (SMOTE). Furthermore, machine learning models are trained on the highest ranked features selected by RF. The results are compared with those provided by machine learning algorithms using full set of features. Experimental results demonstrate that ETC outperforms other models and achieves 0.9262 accuracy value with SMOTE in prediction of heart patient's survival.

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Section: Related Workmentioning

confidence: 99%

Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques

et al. 2021

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“…Finally, the authors suggested using ensemble learning/hybrid models to boost the CVD model's prediction accuracy. Shah et al [14] discussed and experimented with various predictive algorithms like NB, k-NN, DT, and RF where k-NN outperform other algorithms at k=7 in terms of accuracy. They have used the Cleveland dataset and analyzed it with Python Programming language.…”

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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“…Information mining is the investigation of enormous datasets to remove covered up and already obscure examples, connections and information that are hard to distinguish with customary factual strategies (Lee, Liao et al 2000). (Shah, Patel, and Bharti 2020). [7].To robotize the analysis of enormous and complex data, AI calculations and methods have been applied to various clinical datasets.…”

Section: Introductionmentioning

confidence: 99%

Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease

Sameer¹,

Sriramya²

2021

alinteri

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Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.

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Heart Disease Prediction using Machine Learning Techniques

Cited by 401 publications

References 11 publications

Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques

Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques

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

Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease

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