Coronary artery disease diagnosis; ranking the significant features using a random trees model
Javad Hassannataj Joloudari,Edris Hassannataj Joloudari,Hamid Saadatfar,Mohammad GhasemiGol,Seyyed Mohammad Razavi,Amir Mosavi,Narjes Nabipour,Shahaboddin Shamshirband,Laszlo Nadai +8 more
TLDR
The proposed integrated method to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking shows promising results and the study confirms that the RTs model outperforms other models.Abstract:
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.read more
Citations
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Journal ArticleDOI
Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020.
Roohallah Alizadehsani,Abbas Khosravi,Mohamad Roshanzamir,Moloud Abdar,Nizal Sarrafzadegan,Nizal Sarrafzadegan,Davood Shafie,Fahime Khozeimeh,Afshin Shoeibi,Saeid Nahavandi,Maryam Panahiazar,Andrew M. Bishara,Ramin E. Beygui,Rishi Puri,Samir R. Kapadia,Ru San Tan,U. Rajendra Acharya +16 more
TL;DR: The findings demonstrate that AI-based techniques have been increasingly applied for the detection of CAD since 2008 and demonstrate that the attribution of the relative importance of ML features for CAD diagnosis is different among countries.
Journal ArticleDOI
Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives
TL;DR: In this article , the authors provide readers with a review of publications which lie within the intersection of Industry 4.0, Big Data (BD), and healthcare operations and give future perspectives.
Posted ContentDOI
CNN-KCL: Automatic Myocarditis Diagnosis using Convolutional Neural Network Combined with K-means Clustering
Danial Sharifrazi,Roohallah Alizadehsani,Javad Hassannataj Joloudari,Shahab Shamshirband,Sadiq Hussain,Zahra Alizadeh Sani,Fereshteh Hasanzadeh,Afshin Shoaibi,Abdollah Dehzangi,Hamid Alinejad-Rokny +9 more
TL;DR: This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose the Myocarditis and demonstrates that CNNKCL achieves 92.3% in terms of diagnosis myocarditis prediction accuracy which is significantly better than those reported in previous studies.
Journal ArticleDOI
Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning
Javad Hassannataj Joloudari,Mojtaba Haderbadi,Amir Mashmool,Mohammad GhasemiGol,Shahab S. Band,Amir Mosavi +5 more
TL;DR: The experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.
Journal ArticleDOI
Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions
TL;DR: This study targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG, and critically evaluates the previous methods and presents the limitations in these methods.
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