Comparison of the effects of features and classifiers on performance in the cardiovascular disease detection system

İzzet Emir; Yıldız Aydın

Makale detayları

Comparison of the effects of features and classifiers on performance in the cardiovascular disease detection system

Yazarlar:

İzzet Emir | Yıldız Aydın

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Knowledge Representation and Reasoning

Dergi:

Journal of Scientific Reports-A Issue 060, pp.10-18

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342872

pISSN:

2687-6167

Anahtar kelimeler:

Özet:

 This study aims to analyze the effects of features and classifiers in detecting cardiovascular diseases CVD, which remain the leading cause of morbidity and mortality worldwide. Early and accurate detection of CVD significantly affects treatment outcomes. Therefore, the proposed method aims to automatically detect cardiovascular diseases via artificial intelligence. In this research, the performances of artificial intelligence methods for the cardiovascular disease detection problem are analyzed. The dataset used in this study was sourced from the publicly available Kaggle platform. It used for performance analysis in the developed application includes the features of 70000 patients such as age, gender, height, weight, blood pressure, cholesterol, glucose, smoking and alcohol use. These features were classified with Gradient Boosting, XGBoost, SVM, Random Forest, Logistic Regression, Convolutional Neural Network CNN, Recurrent Neural Network RNN, Long Short-Term Memory LSTM methods and performance comparison was performed. In the experimental results, the highest accuracy rate of 72.55 was obtained using the Gradient Boosting method, demonstrating its superior performance in cardiovascular disease detection. In addition, it was observed that the classification performance decreased when the high blood pressure attribute was removed from the dataset, while the removal of other features did not significantly affect the performance.

 This study aims to analyze the effects of features and classifiers in detecting cardiovascular diseases CVD, which remain the leading cause of morbidity and mortality worldwide. Early and accurate detection of CVD significantly affects treatment outcomes. Therefore, the proposed method aims to automatically detect cardiovascular diseases via artificial intelligence. In this research, the performances of artificial intelligence methods for the cardiovascular disease detection problem are analyzed. The dataset used in this study was sourced from the publicly available Kaggle platform. It used for performance analysis in the developed application includes the features of 70000 patients such as age, gender, height, weight, blood pressure, cholesterol, glucose, smoking and alcohol use. These features were classified with Gradient Boosting, XGBoost, SVM, Random Forest, Logistic Regression, Convolutional Neural Network CNN, Recurrent Neural Network RNN, Long Short-Term Memory LSTM methods and performance comparison was performed. In the experimental results, the highest accuracy rate of 72.55 was obtained using the Gradient Boosting method, demonstrating its superior performance in cardiovascular disease detection. In addition, it was observed that the classification performance decreased when the high blood pressure attribute was removed from the dataset, while the removal of other features did not significantly affect the performance.

 This study aims to analyze the effects of features and classifiers in detecting cardiovascular diseases CVD, which remain the leading cause of morbidity and mortality worldwide. Early and accurate detection of CVD significantly affects treatment outcomes. Therefore, the proposed method aims to automatically detect cardiovascular diseases via artificial intelligence. In this research, the performances of artificial intelligence methods for the cardiovascular disease detection problem are analyzed. The dataset used in this study was sourced from the publicly available Kaggle platform. It used for performance analysis in the developed application includes the features of 70000 patients such as age, gender, height, weight, blood pressure, cholesterol, glucose, smoking and alcohol use. These features were classified with Gradient Boosting, XGBoost, SVM, Random Forest, Logistic Regression, Convolutional Neural Network CNN, Recurrent Neural Network RNN, Long Short-Term Memory LSTM methods and performance comparison was performed. In the experimental results, the highest accuracy rate of 72.55 was obtained using the Gradient Boosting method, demonstrating its superior performance in cardiovascular disease detection. In addition, it was observed that the classification performance decreased when the high blood pressure attribute was removed from the dataset, while the removal of other features did not significantly affect the performance.

 This study aims to analyze the effects of features and classifiers in detecting cardiovascular diseases CVD, which remain the leading cause of morbidity and mortality worldwide. Early and accurate detection of CVD significantly affects treatment outcomes. Therefore, the proposed method aims to automatically detect cardiovascular diseases via artificial intelligence. In this research, the performances of artificial intelligence methods for the cardiovascular disease detection problem are analyzed. The dataset used in this study was sourced from the publicly available Kaggle platform. It used for performance analysis in the developed application includes the features of 70000 patients such as age, gender, height, weight, blood pressure, cholesterol, glucose, smoking and alcohol use. These features were classified with Gradient Boosting, XGBoost, SVM, Random Forest, Logistic Regression, Convolutional Neural Network CNN, Recurrent Neural Network RNN, Long Short-Term Memory LSTM methods and performance comparison was performed. In the experimental results, the highest accuracy rate of 72.55 was obtained using the Gradient Boosting method, demonstrating its superior performance in cardiovascular disease detection. In addition, it was observed that the classification performance decreased when the high blood pressure attribute was removed from the dataset, while the removal of other features did not significantly affect the performance.

Makale detayları

Comparison of the effects of features and classifiers on performance in the cardiovascular disease detection system

Yazarlar:

İzzet Emir | Yıldız Aydın

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Knowledge Representation and Reasoning

Dergi:

Journal of Scientific Reports-A Issue 060, pp.10-18

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342872

pISSN:

2687-6167

Cardiovascular disease (CD)

Artificial Intelligence

Detection

CD risk factors

Sayı

Makale detayları

Comparison of the effects of features and classifiers on performance in the cardiovascular disease detection system

Yazarlar: İzzet Emir | Yıldız Aydın

Yayıncı: Kütahya Dumlupinar University

Ana Başlıklar: Knowledge Representation and Reasoning

Dergi: Journal of Scientific Reports-A Issue 060, pp.10-18

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001342872

pISSN: 2687-6167

Cardiovascular disease (CD)

Artificial Intelligence

Detection

CD risk factors

Sayı

Yazarlar:

İzzet Emir | Yıldız Aydın

Yayıncı:

Kütahya Dumlupinar University

Ana Başlıklar:

Knowledge Representation and Reasoning

Dergi:

Journal of Scientific Reports-A Issue 060, pp.10-18

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001342872

pISSN:

2687-6167