Development of an efficient mixed mode heart disease detection model using machine learning and deep learning techniques
Heart disease is one of the major causes for increasing mortality worldwide. This is probing the researchers for development of accurate and efficient diagnostic systems. Hence, a huge research is being carried for detection of heart disease using AI model. Here we propose a mixed mode heart disease...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/05/itmconf_iccp-ci2024_01019.pdf |
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| Summary: | Heart disease is one of the major causes for increasing mortality worldwide. This is probing the researchers for development of accurate and efficient diagnostic systems. Hence, a huge research is being carried for detection of heart disease using AI model. Here we propose a mixed mode heart disease detection model that employs advantages of classical machine learning model and deep learning techniques. This model combines feature selection techniques with a blend of supervised learning methods, such as k-nearest neighbour algorithm, Support Vector Machine (SVM), and Gradient Boosting, to exploit their complementary strengths. This mixed mode approach demonstrated superior performance in comparison to other models that are taken for comparison purpose. Assessment of the model is done by estimating various metrics like, accuracy, sensitivity, and specificity. The findings suggest that this mixed mode method attain accuracy of 96% while other techniques taken for comparison purpose limit their accuracies to a maximum 89%. |
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| ISSN: | 2271-2097 |