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Updated: Jun 27, 2025
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support
Wei Zhou1,2, Hongbo Liu2, Rui Zhou2
1Southwest Medical University, Clinical Medicine School, Luzhou, 646000, Sichuan, China.
Insights
This study introduces a novel hybrid approach combining the locust evolutionary algorithm and support vector machine for improved heart disease diagnosis. This method enhances diagnostic accuracy, offering a more effective solution for early detection and treatment.
Area of Science:
- Computational intelligence
- Medical informatics
- Cardiovascular disease research
Background:
- Heart disease is a leading cause of mortality globally.
- Accurate and timely diagnosis is crucial for effective heart disease management.
- Existing diagnostic methods require enhancement for improved accuracy and efficiency.
Purpose of the Study:
- To develop and evaluate a novel hybrid data mining approach for enhanced heart disease diagnosis.
- To improve the accuracy and efficiency of early heart disease detection and treatment planning.
Main Methods:
- A three-step data normalization process was employed, including data pre-processing to handle outliers.
- The locust evolutionary algorithm was utilized for optimal feature selection.
- A support vector machine classifier was applied for data set classification.
Main Results:
- The proposed hybrid method demonstrated significant improvements in diagnostic accuracy.
- Accuracy increased by 18% compared to Niobizin methods, 30% versus neural networks, and 24% over J48 trees.
- The method effectively integrated feature selection and classification for robust diagnosis.
Conclusions:
- The hybrid locust evolutionary algorithm and support vector machine approach offers a promising solution for accurate heart disease diagnosis.
- This computational method can aid clinicians in making better decisions for patient care.
- Further research can explore this hybrid model for other complex medical diagnostic challenges.
Abstract:
Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.
