Effective Prediction On Heart Disease: Anticipating Heart Disease Using Data Mining Techniques

Rahman, Mafizur; Zahin, Maryam Mehzabin; Islam, Linta

doi:10.1109/icssit46314.2019.8987776

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…The accuracy of our proposed method reaches 98.56%, recall is 99.35%, precision is 97.84%, and F1-score achieves 0.983, with an AUC score of 0.983, proving that this feature selection method and deep neural network are feasible and reliable in predicting heart disease. In the future, we will continue to adjust the depth and parameters of the DNN to enhance the stability of the model as well as research other deep learning optimization techniques to obtain better performance [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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In recent decades, heart disease threatens people’s health seriously because of its prevalence and high risk of death. Therefore, predicting heart disease through some simple physical indicators obtained from the regular physical examination at an early stage has become a valuable subject. Clinically, it is essential to be sensitive to these indicators related to heart disease to make predictions and provide a reliable basis for further diagnosis. However, the large amount of data makes manual analysis and prediction taxing and arduous. Our research aims to predict heart disease both accurately and quickly through various indicators of the body. In this paper, a novel heart disease prediction model is given. We propose a heart disease prediction algorithm that combines the embedded feature selection method and deep neural networks. This embedded feature selection method is based on the LinearSVC algorithm, using the L1 norm as a penalty item to choose a subset of features significantly associated with heart disease. These features are fed into the deep neural network we built. The weight of the network is initialized with the He initializer to prevent gradient varnishing or explosion so that the predictor can have a better performance. Our model is tested on the heart disease dataset obtained from Kaggle. Some indicators including accuracy, recall, precision, and F1-score are calculated to evaluate the predictor, and the results show that our model achieves 98.56%, 99.35%, 97.84%, and 0.983, respectively, and the average AUC score of the model reaches 0.983, confirming that the method we proposed is efficient and reliable for predicting heart disease.

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Section: Discussionmentioning

confidence: 99%

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Zhang

Chen

et al. 2021

Journal of Healthcare Engineering

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“…K-NN performanceis tested on both feature set and feature subset. (36,37,38,39,40,41,42,43,44,45) The suggested procedure produced excellent accuracy. Raju et al (46) modeled a hybrid ML-based HD prediction in different works.…”

Section: Related Workmentioning

confidence: 89%

Design of a Classifier model for Heart Disease Prediction using normalized graph model

Karthiga,

Sinnasamy,

Bharathi

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Heart disease is an illness that influences enormous people worldwide. Particularly in cardiology, heart disease diagnosis and treatment need to happen quickly and precisely. Here, a machine learning-based (ML) approach is anticipated for diagnosing a cardiac disease that is both effective and accurate. The system was developed using standard feature selection algorithms for removing unnecessary and redundant features. Here, a novel normalized graph model (n-GM) is used for prediction. To address the issue of feature selection, this work considers the significant information feature selection approach. To improve classification accuracy and shorten the time it takes to process classifications, feature selection techniques are utilized. Furthermore, the hyper-parameters and learning techniques for model evaluation have been accomplished using cross-validation. The performance is evaluated with various metrics. The performance is evaluated on the features chosen via features representation. The outcomes demonstrate that the suggested n-GM gives 98% accuracy for modeling an intelligent system to detect heart disease using a classifier support vector machine

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“…Principal Component Analysis: Principal component analysis (PCA) is a method to lessen the dimensionality [21] of the dataset for creating predictive paradigms by exploratory analysis [22]. This process can transform data into a different coordinate by conserving the variations in the data.…”

Section: Iv) Logistic Regressionmentioning

confidence: 99%

A Predictive Analysis of Chronic Kidney Disease by Exploring Important Features

Rahman¹,

Sorna²,

Rana³

et al. 2022

IJCDS

Self Cite

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Chronic Kidney Disease is an incurable disease which causes damages to the functions of a kidney gradually. Only proper treatment can prevent the disease from getting worse. Because of proper knowledge about kidney disorders, people had to suffer from this deadly disease. Thus, in this paper, we analyzed certain key features and noticed several interesting relationships with the disease by considering the actual perception of people. We also predict kidney disease by employing various machine learning algorithms including Logistic Regression, Naive Bayes, SVM and KNN. By applying PCA, we observe that there is an improvement in the accuracy for predicting the disease. SVM outperforms other algorithms with 98% accuracy in predicting chronic kidney disease. In future, we will try to find some significant hypothesis that helps us to prevent the disease better.

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Effective Prediction On Heart Disease: Anticipating Heart Disease Using Data Mining Techniques

Cited by 8 publications

References 10 publications

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Heart Disease Prediction Based on the Embedded Feature Selection Method and Deep Neural Network

Design of a Classifier model for Heart Disease Prediction using normalized graph model

A Predictive Analysis of Chronic Kidney Disease by Exploring Important Features

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