Heart disease prediction using autoencoder and DenseNet architecture
Heart disease continues to be a prominent cause of death globally, emphasizing the critical requirement for precise prediction techniques and prompt therapies. This research presents a new method that utilizes the collective capabilities of autoencoder and DenseNet architectures to predict heart ill...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001221 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850119380189315072 |
|---|---|
| author | Norah Saleh Alghamdi Mohammed Zakariah Achyut Shankar Wattana Viriyasitavat |
| author_facet | Norah Saleh Alghamdi Mohammed Zakariah Achyut Shankar Wattana Viriyasitavat |
| author_sort | Norah Saleh Alghamdi |
| collection | DOAJ |
| description | Heart disease continues to be a prominent cause of death globally, emphasizing the critical requirement for precise prediction techniques and prompt therapies. This research presents a new method that utilizes the collective capabilities of autoencoder and DenseNet architectures to predict heart illness. Our study is based on the Heart Disease UCI Cleveland dataset, which includes 13 variables that cover clinical and demographic parameters such as age, sex, cholesterol levels, and exercise-induced angina. The dataset presents issues due to its varied attribute types, including category and numerical variables. Furthermore, our approach tackles these difficulties by utilizing a dense autoencoder model, which produced exceptional outcomes. The Model attained a mean accuracy of 99.67% on the Heart Disease UCI Cleveland dataset. Further testing showed it was resilient, with a test accuracy of 99.99%. In addition, the Model demonstrated outstanding macro precision, macro recall, and macro F1 score, with percentages of 99.98%, 99.97%, and 99.96%, respectively. In addition, our results indicate that combining autoencoder and DenseNet designs shows potential for predicting cardiac disease, with substantial enhancements in accuracy and performance metrics compared to current approaches. This methodology can improve clinical decision-making and patient outcomes in cardiovascular care by accurately finding and defining complex patterns within the data. Notwithstanding these encouraging outcomes, our investigation has constraints. The specific attributes of the dataset utilized may limit the applicability of our findings. Subsequent studies could examine the suitability of our method for various datasets and analyze supplementary variables that may improve forecast precision. Furthermore, it is necessary to conduct prospective validation studies to evaluate our strategy’s practical effectiveness in clinical environments. |
| format | Article |
| id | doaj-art-005fa21376414b76b897c19baa90129e |
| institution | OA Journals |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-005fa21376414b76b897c19baa90129e2025-08-20T02:35:39ZengElsevierEgyptian Informatics Journal1110-86652024-12-012810055910.1016/j.eij.2024.100559Heart disease prediction using autoencoder and DenseNet architectureNorah Saleh Alghamdi0Mohammed Zakariah1Achyut Shankar2Wattana Viriyasitavat3Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh 11671, Saudi ArabiaCollege of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia; Corresponding author.Department of Cyber Systems Engineering, WMG, University of Warwick, Coventry CV74AL, United Kingdom; University Centre for Research & Development, Chandigarh University, Mohali, Punjab, 140413, India; School of Computer Science Engineering, Lovely Professional University, Phagwara 144411, Punjab, India; Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002, India; Center of Research Impact and Outcome, Chitkara University, Punjab, IndiaChulalongkorn Business School, Faculty of Commerce and Accountancy, Chulalongkorn University, ThailandHeart disease continues to be a prominent cause of death globally, emphasizing the critical requirement for precise prediction techniques and prompt therapies. This research presents a new method that utilizes the collective capabilities of autoencoder and DenseNet architectures to predict heart illness. Our study is based on the Heart Disease UCI Cleveland dataset, which includes 13 variables that cover clinical and demographic parameters such as age, sex, cholesterol levels, and exercise-induced angina. The dataset presents issues due to its varied attribute types, including category and numerical variables. Furthermore, our approach tackles these difficulties by utilizing a dense autoencoder model, which produced exceptional outcomes. The Model attained a mean accuracy of 99.67% on the Heart Disease UCI Cleveland dataset. Further testing showed it was resilient, with a test accuracy of 99.99%. In addition, the Model demonstrated outstanding macro precision, macro recall, and macro F1 score, with percentages of 99.98%, 99.97%, and 99.96%, respectively. In addition, our results indicate that combining autoencoder and DenseNet designs shows potential for predicting cardiac disease, with substantial enhancements in accuracy and performance metrics compared to current approaches. This methodology can improve clinical decision-making and patient outcomes in cardiovascular care by accurately finding and defining complex patterns within the data. Notwithstanding these encouraging outcomes, our investigation has constraints. The specific attributes of the dataset utilized may limit the applicability of our findings. Subsequent studies could examine the suitability of our method for various datasets and analyze supplementary variables that may improve forecast precision. Furthermore, it is necessary to conduct prospective validation studies to evaluate our strategy’s practical effectiveness in clinical environments.http://www.sciencedirect.com/science/article/pii/S1110866524001221Heart DiseaseHeart Disease Cleveland datasetMachine-LearningSparse AutoencoderDenseNet |
| spellingShingle | Norah Saleh Alghamdi Mohammed Zakariah Achyut Shankar Wattana Viriyasitavat Heart disease prediction using autoencoder and DenseNet architecture Egyptian Informatics Journal Heart Disease Heart Disease Cleveland dataset Machine-Learning Sparse Autoencoder DenseNet |
| title | Heart disease prediction using autoencoder and DenseNet architecture |
| title_full | Heart disease prediction using autoencoder and DenseNet architecture |
| title_fullStr | Heart disease prediction using autoencoder and DenseNet architecture |
| title_full_unstemmed | Heart disease prediction using autoencoder and DenseNet architecture |
| title_short | Heart disease prediction using autoencoder and DenseNet architecture |
| title_sort | heart disease prediction using autoencoder and densenet architecture |
| topic | Heart Disease Heart Disease Cleveland dataset Machine-Learning Sparse Autoencoder DenseNet |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001221 |
| work_keys_str_mv | AT norahsalehalghamdi heartdiseasepredictionusingautoencoderanddensenetarchitecture AT mohammedzakariah heartdiseasepredictionusingautoencoderanddensenetarchitecture AT achyutshankar heartdiseasepredictionusingautoencoderanddensenetarchitecture AT wattanaviriyasitavat heartdiseasepredictionusingautoencoderanddensenetarchitecture |