Machine-Learning-Based Prediction Models of Coronary Heart Disease Using Naïve Bayes and Random Forest Algorithms

Bemando, Charles; Miranda, Eka; Aryuni, Mediana

doi:10.1109/icsecs52883.2021.00049

Cited by 25 publications

(13 citation statements)

References 9 publications

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“…These three algorithms anticipate and offer statistical findings in a variety of ways. In this experiment, we discovered that Nave Bayes estimated accuracy was higher than that of other algorithms [6].…”

Section: Related Workmentioning

confidence: 81%

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Pal

2022

Biomedical Materials & Devices

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Chronic kidney disease (CKD) is a life-threatening condition that can be difficult to diagnose early because there are no symptoms. The purpose of the proposed study is to develop and validate a predictive model for the prediction of chronic kidney disease. Machine learning algorithms are often used in medicine to predict and classify diseases. Medical records are often skewed. We have used chronic kidney disease dataset from UCI Machine learning repository with 25 features and applied three machine learning classifiers Logistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM) for analysis and then used bagging ensemble method to improve the results of the developed model. The clusters of the chronic kidney disease dataset were used to train the machine learning classifiers. Finally, the Kidney Disease Collection is summarized by category and non-linear features. We get the best result in the case of decision tree with accuracy of 95.92%. Finally, after applying the bagging ensemble method we get the highest accuracy of 97.23%.

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Section: Related Workmentioning

confidence: 81%

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Pal

2022

Biomedical Materials & Devices

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“…For the CHD dataset, Refs. [ 22 , 31 , 32 , 33 , 34 ] are selected. The study [ 22 ] proposed a hybrid RF with a linear model (HRFLM) for heart disease prediction.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, Ref. [ 32 ] used CHD to experiment with heart disease detection. The authors deployed Bernoulli Naive Bayes (BNB) and RF for prediction.…”

Section: Resultsmentioning

confidence: 99%

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

et al. 2022

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Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets.

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“…For instance, Sun et al (2021) used hybrid approaches to predict coronary Heart disease using Gaussian Naïve Bayes, Bernoulli Naïve Bayes, and Random Forest (RF) algorithms [ 111 ]. Bemando et al (2021) adopted CNN and SVM to automate the diagnosis of Alzheimer’s disease and mild cognitive impairment [ 41 ]. Saxena et al (2019) used KNN and Decision Tree (DT) in Heart disease diagnosis [ 131 ]; Elsalamony (2018) employed Neural Networks (NN) and SVM in detecting Anaemia disease in human red blood cells [ 132 ].…”

Section: Algorithm and Dataset Analysismentioning

confidence: 99%

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

2022

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Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.

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Machine-Learning-Based Prediction Models of Coronary Heart Disease Using Naïve Bayes and Random Forest Algorithms

Cited by 25 publications

References 9 publications

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Chronic Kidney Disease Prediction Using Machine Learning Techniques

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

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