scholarly journals Environmental factors prediction in preterm birth using comparison between logistic regression and decision tree methods: An exploratory analysis

2021 ◽  
Vol 4 (1) ◽  
pp. 100216
Author(s):  
Rakesh Kumar Saroj ◽  
Madhu Anand
Keyword(s):  
Logistic Regression ◽  
Preterm Birth ◽  
Environmental Factors ◽  
Decision Tree ◽  
Exploratory Analysis ◽  
Tree Methods ◽  
Decision Tree Methods

scholarly journals Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods

2017 ◽  
Vol 8 (3) ◽  
pp. 195-200 ◽  
Author(s):  
Payam Amini ◽  
Saman Maroufizadeh ◽  
Reza Omani Samani ◽  
Omid Hamidi ◽  
Mahdi Sepidarkish
Keyword(s):  
Logistic Regression ◽  
Preterm Birth ◽  
Decision Tree ◽  
Tree Methods ◽  
Decision Tree Methods

Comparisons of decision tree methods using water data

2016 ◽  
Vol 46 (4) ◽  
pp. 2924-2934 ◽  
Author(s):  
Muhammad Azam ◽  
Muhammad Aslam ◽  
Khushnoor Khan ◽  
Anwar Mughal ◽  
Awais Inayat
Keyword(s):  
Decision Tree ◽  
Tree Methods ◽  
Decision Tree Methods

An Approach for ECG Characterization and Classification Using the Combination of Wavelet Transform and Decision Tree Methods

2012 ◽  
Vol 1 (3) ◽  
pp. 72-81
Author(s):  
Faiza Charfi ◽  
Ali Kraiem
Keyword(s):  
Wavelet Transform ◽  
Decision Tree ◽  
High Reliability ◽  
Principal Component ◽  
Component Analysis ◽  
Ecg Signals ◽  
Interaction Detection ◽  
Fast Independent Component Analysis ◽  
Tree Methods ◽  
Decision Tree Methods

A new automated approach for Electrocardiogram (ECG) arrhythmias characterization and classification with the combination of Wavelet transform and Decision tree classification is presented. The approach is based on two key steps. In the first step, the authors adopt the wavelet transform to extract the ECG signals wavelet coefficients as first features and utilize the combination of Principal Component Analysis (PCA) and Fast Independent Component Analysis (FastICA) to transform the first features into uncorrelated and mutually independent new features. In the second step, they utilize some decision tree methods currently in use: C4.5, Improved C4.5, CHAID (Chi - Square Automatic Interaction Detection) and Improved CHAID for the classification of ECG signals, which are taken, from the MIT-BIH database, including normal subjects and subjects affected by arrhythmia. The authors’ results suggest the high reliability and high classification accuracy of C4.5 algorithm with the bootstrap aggregation.

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