The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data
The crux of effective management is in the process of decision-making, which is contingent upon the availability of information and effective communication. The fundamental responsibility of executives is to provide the necessary information to facilitate sound management decisions. This study seeks...
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Bilijipub publisher
2023-12-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_186529_5d68d371e7146f4f4564dea064b453d9.pdf |
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| author | Valentino Blanco Aitana Iglesias |
| author_facet | Valentino Blanco Aitana Iglesias |
| author_sort | Valentino Blanco |
| collection | DOAJ |
| description | The crux of effective management is in the process of decision-making, which is contingent upon the availability of information and effective communication. The fundamental responsibility of executives is to provide the necessary information to facilitate sound management decisions. This study seeks to utilize hospital managers' outcomes from data mining of hospital information systems to develop an intelligent model using machine learning techniques. The objective is to enhance the accuracy of predictions and facilitate more effective decision-making in patient treatment, recognizing the significance of hospital managers' decision-making approaches in advancing hospital goals and addressing patients' treatment challenges. The dataset used in this research pertains to the demographic and clinical information of 297 individuals. This data was obtained from the UCI website's data warehouse and encompasses 14 distinct variables. The three models, namely "k-means, support vector machine, and neural network," are extensively used classification methods in the domains of data mining and machine learning. These models have been applied to forecast cardiac disease, and their predictive performance has been evaluated and compared. The findings demonstrate that the neural network model, characterized by a multi-layered perceptron architecture, achieved a classification accuracy of 89.9% when applied to the test dataset. However, the support vector machine using the radial basis function kernel demonstrates enhanced accuracy, achieving a level of 93%. |
| format | Article |
| id | doaj-art-c9fc6f7d5bc949c7a7d026db1a6ee569 |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-c9fc6f7d5bc949c7a7d026db1a6ee5692025-02-12T08:47:32ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632023-12-010020412113110.22034/aeis.2023.427722.1149186529The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical DataValentino Blanco0Aitana Iglesias1Faculty of Agronomy, University of Buenos Aires, Buenos Aires, ArgentinaDepartment of Electrical Engineering, Facultad Regional San Nicolás (FRSN), Universidad Tecnológica Nacional (UTN) San Nicolás, Buenos Aire, ArgentinaThe crux of effective management is in the process of decision-making, which is contingent upon the availability of information and effective communication. The fundamental responsibility of executives is to provide the necessary information to facilitate sound management decisions. This study seeks to utilize hospital managers' outcomes from data mining of hospital information systems to develop an intelligent model using machine learning techniques. The objective is to enhance the accuracy of predictions and facilitate more effective decision-making in patient treatment, recognizing the significance of hospital managers' decision-making approaches in advancing hospital goals and addressing patients' treatment challenges. The dataset used in this research pertains to the demographic and clinical information of 297 individuals. This data was obtained from the UCI website's data warehouse and encompasses 14 distinct variables. The three models, namely "k-means, support vector machine, and neural network," are extensively used classification methods in the domains of data mining and machine learning. These models have been applied to forecast cardiac disease, and their predictive performance has been evaluated and compared. The findings demonstrate that the neural network model, characterized by a multi-layered perceptron architecture, achieved a classification accuracy of 89.9% when applied to the test dataset. However, the support vector machine using the radial basis function kernel demonstrates enhanced accuracy, achieving a level of 93%.https://aeis.bilijipub.com/article_186529_5d68d371e7146f4f4564dea064b453d9.pdfdata miningk-meansneural networksupport vector machinesheart disease |
| spellingShingle | Valentino Blanco Aitana Iglesias The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data Advances in Engineering and Intelligence Systems data mining k-means neural network support vector machines heart disease |
| title | The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data |
| title_full | The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data |
| title_fullStr | The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data |
| title_full_unstemmed | The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data |
| title_short | The Use of Neural Networks in the Diagnosis of Heart Failure Via the Analysis of Medical Data |
| title_sort | use of neural networks in the diagnosis of heart failure via the analysis of medical data |
| topic | data mining k-means neural network support vector machines heart disease |
| url | https://aeis.bilijipub.com/article_186529_5d68d371e7146f4f4564dea064b453d9.pdf |
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