Towards Application of Machine Learning in Classification and Prediction of Heart Disease

Sajja, Guna Sekhar; Mustafa, Malik; Phasinam, Khongdet; Kaliyaperumal, Karthikeyan; Ventayen, Randy Joy Magno; Kassanuk, Thanwamas

doi:10.1109/icesc51422.2021.9532940

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…They handle multiple parameters and successfully extract knowledge from the data set. For example, SVM is the most reliable process, followed by KNN, Random Forest, Decision Tree, and ID3 algorithms [43]. An improved version of the K-means neighbor classifier has been used to guarantee more accuracy in predicting heart disease early on [44].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

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This study addresses the problem of heart disease detection, a critical concern in public health. The research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms in diagnosing heart disease using a dataset comprising 14 features. The primary objective is to determine whether CNNs can provide more accurate and reliable results than traditional techniques. The research employs rigorous preprocessing, normalizing relevant features, and splits the dataset into an 80-20 training-testing split. The model is trained for 300 epochs with a batch size of 64, and performance evaluation is conducted using confusion matrices and classification reports. The results reveal that the CNN model achieved a remarkable accuracy of 100%, demonstrating its potential to outperform conventional machine learning algorithms. These findings emphasize the significance of deep learning techniques in improving heart disease diagnostics, although further research is needed to optimize CNN models and address interpretability concerns for practical implementation in healthcare settings.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Mulyani,

Wijaya,

Trinidya

2024

COMPLETE

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“…Several authors have investigated (or included characteristics) that may have an impact on VR-learning and longdistance learning, as well as their success in academic institutions. Service Quality [49] [50], Information [51], System Quality [52], Culture Factor [53], Net Benefits [54], Social Influence, Intention to Use/Usage [55] , and User Satisfaction [55]. Despite its flaws, several authors have begun to use re-specified editions of the D&M IS Success Model for institutional assessment [56].…”

Section: Research Studiesmentioning

confidence: 99%

Untitled

2022

GSC Adv. Eng. Technol.

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The goal of this study is to determine the importance of IS success and its relationships with the intent to use, utilization, user happiness, and net benefits of VR-learning at six Jordanian universities. This study focuses on the use of cultural and social elements that have been identified as important in improving students' learning processes, with the goal of persuading university administrators to pay attention to factors that influence the usage of VR-learning. At Jordan, however, there has been little success with VR-learning adoption in higher education institutions. In addition, only a few research have looked at the aspects that influence the success of VR-learning deployment.

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“…Decision trees are a method of classification and prediction. This algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms (Sajja et al, 2021). The advantages of the C4.5 algorithm can produce a decision tree that is easy to interpret, has an acceptable level of accuracy, is efficient in handling discrete and numeric type attributes (Yendrizal, 2021).…”

Section: Introductionmentioning

confidence: 99%

WEB Based Social Assistance Recipient Selection Application Based on Community Education Level With C4.5 Algorithm

Nurdiansah¹,

Usman²,

Harlina³

et al. 2022

ceddi j. edu.

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The Koramil 1408-02/Tallo office is in charge of distributing social assistance recipients. The social assistance is in the form of basic food assistance for the community and MSMEs for people who have micro-enterprises, where each aid has different criteria in determining the recipient. Regarding the existing social assistance and having different criteria in determining acceptance, including the level of education, of course, the staff of the 1408-02/Tallo Koramil Office will find it difficult to carry out the selection process. Based on existing data mining, beneficiaries can be made to assist beneficiaries who will assist the Koramil 1408-02/Tallo staff. Classification can be done using the C4.5 algorithm. This study aims to determine the recipients of social assistance and apply it in the development of the recipient system of social assistance, including the level of community education. This makes it easier to determine recipients of social assistance and the results of this study are applications for social assistance recipients, so that they can overcome the problems that exist in the current system. Based on the results of the research that has been done on the design of this application, it has succeeded in helping the Koramil Office 1408-02/Tallo in selecting social assistance recipients based on the recipient's education and functioning as expected.

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Towards Application of Machine Learning in Classification and Prediction of Heart Disease

Cited by 15 publications

References 10 publications

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Enhancing Heart Disease Detection Using Convolutional Neural Networks and Classic Machine Learning Methods

Untitled

WEB Based Social Assistance Recipient Selection Application Based on Community Education Level With C4.5 Algorithm

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