Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Karthick, K.; Aruna, S.; Samikannu, Ravi; Kuppusamy, Ramya; Teekaraman, Yuvaraja; Thelkar, Amruth Ramesh

doi:10.1155/2022/6517716

Cited by 47 publications

(25 citation statements)

References 26 publications

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“…The research reported in [6] also uses statistical models such as SVM, Gaussian Naïve Bayes (GNB), logistic regression, LightGBM, CGBoost, and random forest (RF) to create a classifier that is tested and trained on the Cleveland Clinic Foundation for Heart Disease dataset. The accuracy of the models is measured using performance matrices and confusion matrices.…”

Section: Classical ML Techniquesmentioning

confidence: 99%

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

et al. 2023

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Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. The aim of the paper is to investigate the potential of the classical, evolutionary, and deep learning-based methods to diagnose CVDs and to introduce a couple of complex hybrid techniques that combine hyper-parameter optimization algorithms with two of the most successful classification procedures: support vector machines (SVMs) and Long Short-Term Memory (LSTM) neural networks. The resulting algorithms were tested on two public datasets: the data recorded by the Cleveland Clinic Foundation for Heart Disease together with its extension Statlog, two of the most significant medical databases used in automated prediction. A long series of simulations were performed to assess the accuracy of the analyzed methods. In our experiments, we used F1 score and MSE (mean squared error) to compare the performance of the algorithms. The experimentally established results together with theoretical consideration prove that the proposed methods outperform both the standard ones and the considered statistical methods. We have developed improvements to the best-performing algorithms that further increase the quality of their results, being a useful tool for assisting the professionals in diagnosing CVDs in early stages.

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Section: Classical ML Techniquesmentioning

confidence: 99%

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

et al. 2023

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“…Te 10-fold cross validation has been performed and the mean accuracy of 19 MLAs has been listed in Table 3. Ensemble methods [26] such as AdaBoost classifer, Bagging classifer, and extra tree classifer, generalised linear models [27] like logistic regression, passive aggressive classifer, Ridge classifer, stochastic gradient descent classifer, and perceptron, Navies Bayes models [28] like Bernoulli and Gaussian MLA, kNN, and SVM algorithms [29], tree-based methods [30] such as DT classifer and extra tree classifer, and discriminant analysis methods [31] such as linear and quadratic discriminant analysis. Gaussian process MLAs have been evaluated with 10-fold cross-validation.…”

Section: Machine Learning Algorithm (Mla) Selectionmentioning

confidence: 99%

“…Tere are k training and testing iterations. In iteration "i" the test set is partition F i , while the remaining segments, subgroups, or folds are used to train the model collectively [29]. Table 7 and Figure 8 show the 10-fold cross validation accuracy of the three MLAs.…”

Section: Model Performance With Cross-validation (Cv)mentioning

confidence: 99%

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Krishnan¹,

Aruna

Karthick³

et al. 2023

Scientific Programming

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Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset’s attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent.

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“…Compared with conventional methods, this system can avoid human errors and save time. This application shows that enough historical data and precise prediction promotes the survival rate of the patients [4]. Classification methods is one effective way to supervise the machine learning in prediction based on historical data [5].…”

Section: Introductionmentioning

confidence: 97%

Exploring the key factors related to the risk of heart disease by applying classification methods

You

2023

Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022)

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With the development of artificial intelligence, the application of machine learning has played a key role in medicine, finance, retail, tourism, etc. This article scientifically assesses the application of machine learning in medical diagnostic functions to determine the risk of heart disease in patients by building effective machine learning models derived by using classification methods to select the key symptoms and living factors. 2020 heart disease datasets were applied and 19 indicators in the datasets have been evaluated to predict whether a patient has a risk of having heart disease. To make a more accurate correlation between these indicators and heart disease, the indicators are developed by feature transformation, normalization, and feature selection after cleaning raw datasets. The linear regression and random forest classification algorithms were applied and tested. The result shows that the test of random forest model with oversampling revision has a higher accuracy of 91.00%.

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Implementation of a Heart Disease Risk Prediction Model Using Machine Learning

Cited by 47 publications

References 26 publications

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

Classical, Evolutionary, and Deep Learning Approaches of Automated Heart Disease Prediction: A Case Study

Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm

Exploring the key factors related to the risk of heart disease by applying classification methods

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