A Clinical support system for Prediction of Heart Disease using Machine Learning Techniques

Hamdaoui, Halima El; Boujraf, Saïd; Chaoui, Nour El Houda; Maâroufi, Mustapha

doi:10.1109/atsip49331.2020.9231760

Cited by 33 publications

(11 citation statements)

References 14 publications

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“…These data were all collected from the UCI machine learning repository. Further studies have used the Cleveland dataset only since not lacking values [11], [13], [16]- [19]. In contrast, other datasets showed more than 90% of some attributes' missing values which might compromise the accuracy and the quality of results, e.g "thal"and "ca" attributes that shown to have high correlation with the output attribute.…”

Section: Resultsmentioning

confidence: 99%

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

et al. 2021

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heart disease is a major cause of death worldwide. Thus, diagnosis and prediction of heart disease remain mandatory. Clinical decision support systems based on machine learning techniques have become the primary tool to assist clinicians and contribute to automated diagnosis. This paper aims to predict heart disease using Random Forest algorithm enhanced with the boosting algorithm Adaboost. The model is trained and tested on University of California Irvine (UCI) Cleveland and Statlog heart disease datasets using the most relevant features 14 attributes. The result shows that Random Forest algorithm combined with AdaBoost algorithm achieved higher accuracy than applying only Radom Forest algorithm, 96.16%, 95.98%, respectively. We compare our suggested model to report machine learning classifiers. Indeed, the obtained result is supporting the efficiency and validity of our model. Besides, the proposed model achieved high accuracy compared to existing studies in the literature that confirmed that a clinical decision support system could be used to predict heart disease based on machine learning algorithms.

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Section: Resultsmentioning

confidence: 99%

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

et al. 2021

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“…Hamdaoui et al [16] proposed a clinical predictive system for Cardiovascular disease. They have used various algorithms like NB, k-NN, SVM, RF, and DT and then applied them to the Cleveland dataset.…”

Section: Related Workmentioning

confidence: 99%

“…An experiment was carried out on R Studio and the result concluded that HRFLM produced better accuracy (88.47%) than other classifiers. [6], [17], [19], [7]- [11], [13], [14], [16] 11 NB [7], [8], [10], [11], [13], [14], [16], [18], [20] 9…”

Section: Related Workmentioning

confidence: 99%

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Analyzing Predictive Algorithms in Data Mining for Cardiovascular Disease using WEKA Tool

Aman¹,

Chhillar²

2021

IJACSA

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Cardiovascular Disease (CVD) is the foremost cause of death worldwide that generates a high percentage of Electronic Health Records (EHRs). Analyzing these complex patterns from EHRs is a tedious process. To address this problem, Medical Institutions requires effective Predictive Algorithms for the Prognosis and Diagnosis of the Patients. Under this work, the current state-of-the-art studied to identify leading Predictive Algorithms. Further, these algorithms namely Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), AdaBoost and k-Nearest Neighbors (k-NN) analyzed against the two datasets on opensource WEKA software. This work used two similar structured datasets i.e., Statlog Dataset and Cleveland Dataset. For Pre-Processing of Datasets, The missing values were replaced with the Mean value and later 10 Fold Cross-Validation was utilized for the evaluation. The result of the performance analysis showed that SVM outperforms other algorithms against both datasets. SVM showed an accuracy of 84.156% against the Cleveland dataset and 84.074% against the Statlog dataset. LR showed a ROC Area of 0.9 against both datasets. The findings of the work will help Health Institutions to understand the importance and usage of Predictive Algorithms for the automatic prediction of CVD based on the symptoms.

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“…In addition to the above traits, lifestyle behaviors such as eating habits, inactivity, and obesity are important risk factors as well. Heart disease, angina, coronary congestion, cardiomyopathy, congenital heart disease, arrhythmias, and myocarditis are only a few of the many kinds of heart disease [5][6][7]. To get the project going, we could make use of a wide range of approaches and methods.…”

Section: Introductionmentioning

confidence: 99%

Parametric Analysis of Chronic Heart Disease (CHD) Using Machine Learning

2022

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When it comes to mobility issues and heart disease, a machine learning computer can make critical predictions. The remainder of the body is the largest and most concentrated organ in the human body when compared to the heart. Predicting cardiac disease via data analysis is a critical medical endeavor. The medical business throughout the world recycles machine learning. When it comes to machine learning, whether a person has mobility abnormalities or heart ailments is a critical consideration. In medical facilities, data analysis aids in the prediction of more information and the prevention of certain diseases. The study paper's major objective is to forecast a patient's heart condition using a machine learning method such as a random forest, which is the most reliable. Every month, a huge amount of patient data is archived. The information that has been collected can be utilized to make predictions about what illnesses will arise in the future. Certain data mining and machine learning technologies are utilized to anticipate cardiac illness, such as artificial neural networks (ANN), decision trees, fuzzy logic, K-Nearest neighbors (KNN), naïve bays and vector support equipment, for example (SVM). The final goal of this research is to examine the best python learning-based logistic regression model. It is a machine learning model. The heart disease data sets were utilized by the UCI machine learning depot.

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A Clinical support system for Prediction of Heart Disease using Machine Learning Techniques

Cited by 33 publications

References 14 publications

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

Improving Heart Disease Prediction Using Random Forest and AdaBoost Algorithms

Analyzing Predictive Algorithms in Data Mining for Cardiovascular Disease using WEKA Tool

Parametric Analysis of Chronic Heart Disease (CHD) Using Machine Learning

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