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Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest
Muhammad Amir Khan1, Tehseen Mazhar2, Muhammad Mateen Yaqoob3
1School of Computing Sciences, College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia.
Insights
This study introduces a machine learning model for early heart disease diagnosis, using a modified bee algorithm for optimal feature selection to improve accuracy and reduce training time.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Informatics
Background:
- Heart disease is a major global health concern.
- Early diagnosis is crucial for effective patient management.
- Machine learning offers promising avenues for improving diagnostic accuracy.
Purpose of the Study:
- To develop and evaluate a novel machine learning framework for accurate heart disease prediction.
- To optimize feature selection for enhanced classification performance.
- To reduce computational complexity in heart disease diagnosis.
Main Methods:
- A hybrid framework combining Modified Artificial Bee Colony (M-ABC) for attribute selection and k-Nearest Neighbors (KNN) for classification was proposed.
- A modified bee algorithm was employed to identify the most informative features from the dataset.
- Feature selection was integrated into the classification-training phase to retain significant attributes.
Main Results:
- The proposed M-ABC and KNN framework demonstrated improved classification accuracy for heart disease prediction.
- The attribute selection process effectively identified optimal features, enhancing model performance.
- A significant reduction in classifier training time was observed due to efficient feature selection.
Conclusions:
- The developed machine learning framework provides an effective tool for early and accurate heart disease diagnosis.
- Optimized feature selection using modified bee algorithms enhances predictive model performance and efficiency.
- This approach supports clinicians in making informed decisions for better patient outcomes in cardiovascular care.
Abstract:
Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was done using a modified bee algorithm. Using the proposed model, practitioners can accurately predict heart disease and make informed decisions about patient health. In our study, we have proposed a framework based on Modified Artificial Bee Colony (M-ABC) and k-Nearest Neighbors (KNN) for predicting the optimal feature selection to obtain better accuracy. Using a modified bee algorithm, this paper focuses on identifying the optimal subset of attributes from the dataset. Specifically, during the classification-training phase, only the features that provide significant information are retained. The proposed study not only improves classification accuracy but also reduces training time for classifiers.
