计算机科学
人工智能
降维
主成分分析
人工神经网络
机器学习
分类器(UML)
逻辑回归
特征提取
预测建模
维数之咒
深度学习
模式识别(心理学)
数据挖掘
作者
Diman Hassan,Haval I. Hussein,Masoud M. Hassan
标识
DOI:10.1016/j.bspc.2022.104019
摘要
• An effective model for predicting heart disease is proposed, with promising results. • Deep Neural Networks are used as a feature extraction approach. • PCA is used for dimensionality reduction and Logistic Regression classifier for model prediction. • The proposed approach outperformed state-of-the-art approaches for diagnosing heart disease. Heart Disease (HD) is often regarded as one of the deadliest human diseases. Therefore, early prediction of HD risks is crucial for prevention and treatment. Unfortunately, current clinical procedures for diagnosing HD are costly and often require an expert level of intervention. In response to this issue, researchers have recently developed various intelligent systems for the automated diagnosis of HD. Among the developed approaches, those based on artificial neural networks (ANNs) have gained more popularity due to their promising prediction results. However, to the authors’ knowledge, no research has attempted to exploit ANNs for feature extraction. Hence, research into bridging this gap is worthwhile for more excellent predictions. Motivated by this fact, this research proposes a new approach for HD prediction, utilizing a pre-trained Deep Neural Network (DNN) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Logistic Regression (LR) for prediction. Cleveland, a publicly accessible HD dataset, was used to investigate the efficacy of the proposed approach (DNN + PCA + LR). Experimental results revealed that the proposed approach performs well on both the training and testing data, with accuracy rates of 91.79% and 93.33%, respectively. Furthermore, the proposed approach exhibited better performance when compared with the state-of-the-art approaches under most of the evaluation metrics used.
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