Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning
Our research aims to present a comprehensive study of machine learning algorithms and deep learning advancements in medical field systems for decision making. Present study examines the idea of extracting most important risk factors from given medical data, which has major impact in the increase of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Measurement: Sensors |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917424003817 |
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| author | T. Swathi Priyadarshini Mohd Abdul Hameed |
| author_facet | T. Swathi Priyadarshini Mohd Abdul Hameed |
| author_sort | T. Swathi Priyadarshini |
| collection | DOAJ |
| description | Our research aims to present a comprehensive study of machine learning algorithms and deep learning advancements in medical field systems for decision making. Present study examines the idea of extracting most important risk factors from given medical data, which has major impact in the increase of severity condition of heart stroke. Three experimental prediction models are developed when k-means clustering is collaborated with classification which includes machine learning algorithms like Naïve Bayes, Decision Tree and a deep learning algorithm Artificial Neural Network. A detailed comparison analysis is done by evaluating performance metrics like sensitivity, specificity, accuracy, and AUC-ROC scores. Out of the three, k-means with Artificial Neural Network model outperformed with sensitivity 0.89, specificity 0.89, and accuracy of 0.90 in comparison with machine learning classifiers. The challenges of perfect balancing of sensitivity and specificity is achieved by AUC-ROC score of 0.96, which is the best possible result till now. |
| format | Article |
| id | doaj-art-1b599df32d064f3b994c5507df239a0b |
| institution | Kabale University |
| issn | 2665-9174 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj-art-1b599df32d064f3b994c5507df239a0b2025-01-26T05:04:51ZengElsevierMeasurement: Sensors2665-91742025-02-0137101405Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learningT. Swathi Priyadarshini0Mohd Abdul Hameed1Corresponding author.; Department of Computer Science and Engineering, University College of Engg, Osmania University, Hyderabad, Telangana, IndiaDepartment of Computer Science and Engineering, University College of Engg, Osmania University, Hyderabad, Telangana, IndiaOur research aims to present a comprehensive study of machine learning algorithms and deep learning advancements in medical field systems for decision making. Present study examines the idea of extracting most important risk factors from given medical data, which has major impact in the increase of severity condition of heart stroke. Three experimental prediction models are developed when k-means clustering is collaborated with classification which includes machine learning algorithms like Naïve Bayes, Decision Tree and a deep learning algorithm Artificial Neural Network. A detailed comparison analysis is done by evaluating performance metrics like sensitivity, specificity, accuracy, and AUC-ROC scores. Out of the three, k-means with Artificial Neural Network model outperformed with sensitivity 0.89, specificity 0.89, and accuracy of 0.90 in comparison with machine learning classifiers. The challenges of perfect balancing of sensitivity and specificity is achieved by AUC-ROC score of 0.96, which is the best possible result till now.http://www.sciencedirect.com/science/article/pii/S2665917424003817Artificial neural networkAccuracyArea under ROCBig dataDeep learningDecision making |
| spellingShingle | T. Swathi Priyadarshini Mohd Abdul Hameed Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning Measurement: Sensors Artificial neural network Accuracy Area under ROC Big data Deep learning Decision making |
| title | Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning |
| title_full | Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning |
| title_fullStr | Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning |
| title_full_unstemmed | Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning |
| title_short | Collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning |
| title_sort | collaboration of clustering and classification techniques for better prediction of severity of heart stroke using deep learning |
| topic | Artificial neural network Accuracy Area under ROC Big data Deep learning Decision making |
| url | http://www.sciencedirect.com/science/article/pii/S2665917424003817 |
| work_keys_str_mv | AT tswathipriyadarshini collaborationofclusteringandclassificationtechniquesforbetterpredictionofseverityofheartstrokeusingdeeplearning AT mohdabdulhameed collaborationofclusteringandclassificationtechniquesforbetterpredictionofseverityofheartstrokeusingdeeplearning |