The Hybrid Logistics Regression-Artificial Neural Network and Multivariate Adaptive Regression Splines-Artificial Neural Network Modeling Schemes for Heart Disease Classification

Yuehjen E. Shao; Chia-Ding Hou; Ya-Chi Chan; Hung-Tzu Lin

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The Hybrid Logistics Regression-Artificial Neural Network and Multivariate Adaptive Regression Splines-Artificial Neural Network Modeling Schemes for Heart Disease Classification

机译：心脏病分类的混合后勤回归-人工神经网络和多元自适应回归样条-人工神经网络建模方案

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Heart disease is the leading cause of death in men and women in most countries in the world. People must pay attention to heart disease risk factors. Although genetics plays a role, some lifestyle factors are also crucial for heart disease risk. The traditional approaches use thirteen risk factors or explanatory variables to classify heart disease. Diverging from existing approaches, the present study proposes a new hybrid modeling scheme to obtain different sets of explanatory variables, and the proposed hybrid models are able to effectively classify heart disease. The proposed hybrid models consist of the logistics regression (LR), multivariate adaptive regression splines (MARS) and the artificial neural network (ANN) components. The initial stage is to use the LR or MARS technique to reduce the set of explanatory variables. The remaining variables are then served as inputs to the ANN in the second stage. A real data set of heart disease is used for demonstration of the development of the proposed hybrid models. The modeling results reveal that the proposed hybrid scheme is able to effectively classify heart disease and outperform the typical, single stage ANN method.

机译：在世界上大多数国家，心脏病是导致男性和女性死亡的主要原因。人们必须注意心脏病的危险因素。尽管遗传起着一定的作用，但某些生活方式因素对于心脏病风险也至关重要。传统方法使用十三种危险因素或解释变量对心脏病进行分类。与现有方法不同，本研究提出了一种新的混合建模方案，以获取不同的解释变量集，并且所提出的混合模型能够有效地对心脏病进行分类。提出的混合模型由物流回归（LR），多元自适应回归样条（MARS）和人工神经网络（ANN）组成。初始阶段是使用LR或MARS技术来减少解释变量集。然后，其余变量在第二阶段用作ANN的输入。真实的心脏病数据集用于证明所提出的混合模型的发展。建模结果表明，提出的混合方案能够有效地对心脏病进行分类，并且优于典型的单阶段ANN方法。

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《Advanced Science Letters》 |2013年第11期|共4页
作者
Yuehjen E. Shao; Chia-Ding Hou; Ya-Chi Chan; Hung-Tzu Lin;
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正文语种 eng
中图分类工业技术;
关键词
Hybrid; Logistics Regression; MARS; Artificial Neural Network; Heart Diseas;

机译：混合;物流回归;MARS;人工神经网络;心脏病;

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1. The Hybrid Logistics Regression-Artificial Neural Network and Multivariate Adaptive Regression Splines-Artificial Neural Network Modeling Schemes for Heart Disease Classification [J] . Yuehjen E. Shao, Chia-Ding Hou, Ya-Chi Chan, Advanced Science Letters . 2013,第11期

机译：心脏病分类的混合后勤回归-人工神经网络和多元自适应回归样条-人工神经网络建模方案
2. Landslide Susceptibility Mapping in the Commune of Oudka, Taounate Province, North Morocco: A Comparative Analysis of Logistic Regression, Multivariate Adaptive Regression Spline, and Artificial Neural Network Models [J] . Benchelha Said, Aoudjehane Hasnaa Chennaoui, Hakdaoui Mustapha, Environmental & Engineering Geoscience . 2020,第2期

机译：北摩洛哥陶氏省奥德卡公报的滑坡易感性 - 对逻辑回归，多变量自适应回归条和人工神经网络模型的比较分析
3. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines [J] . Tian-Shyug Lee, I-Fei Chen Expert Systems with Application . 2005,第4期

机译：基于人工神经网络和多元自适应回归样条的两阶段混合信用评分模型
4. Back-Propagation Neural Network Versus Logistic Regression in Heart Disease Classification [C] . Shrinivas D. Desai, Shantala Giraddi, Prashant Narayankar, International Conference on Advanced Computing and Communication Technologies . 2019

机译：背部传播神经网络与心脏病分类中的逻辑回归
5. A comparative analysis of artificial neural networks, classification trees, and multivariate linear regression for predicting retail employee tenure and turnover [D] . Ostberg, David Edward 2005

机译：人工神经网络，分类树和多元线性回归的比较分析，用于预测零售雇员的任期和营业额
6. Modeling mortality in the intensive care unit: comparing the performance of a back-propagation associative-learning neural network with multivariate logistic regression. [O] . G. S. Doig, K. J. Inman, W. J. Sibbald, 1993

机译：重症监护病房的死亡率建模：将反向传播联想学习神经网络与多元逻辑回归的性能进行比较。
7. Comparative Study of Classification Techniques (SVM, Logistic Regression and Neural Networks) to Predict the Prevalence of Heart Disease [O] . Divyansh Khanna, Rohan Sahu, Veeky Baths, 2015

机译：分类技术（SVM，逻辑回归和神经网络）预测心脏病患病率的比较研究
8. Comparison of Artificial Neural Networks, Logistic Regressions, and Classification Trees for Modeling Mental Workload in Real-Time. [R] . Fong, A., Sibley, C., Cole, A., 2010

机译：用于实时建模心理工作量的人工神经网络，Logistic回归和分类树的比较。

The Hybrid Logistics Regression-Artificial Neural Network and Multivariate Adaptive Regression Splines-Artificial Neural Network Modeling Schemes for Heart Disease Classification

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