Enhancing Machine Learning Classifier Efficiency for Heart Disease Prediction Through Bacterial Colony Optimization
By
Tanver Ahmed
Md. Muktar Hossain
Mohammad Kasedullah
Md. Toufikul Islam
A. S. M Delwar Hossain
Masud Ibn Afjal
January 1, 2026
Clinical Scorecard: Enhancing Machine Learning Classifier Efficiency for Heart Disease Prediction Through Bacterial Colony Optimization
At a Glance
Category Detail
Condition Cardiovascular Diseases (CVDs) Key Mechanisms Machine Learning and Deep Learning techniques for early detection and prediction of heart disease. Target Population Individuals at risk for cardiovascular diseases, including those with risk factors such as obesity, diabetes, and hypertension. Care Setting Healthcare facilities utilizing AI and ML for diagnostic purposes.
Key Highlights
CVDs are responsible for 32% of global fatalities, with heart attacks and strokes being the most common causes. Machine Learning enhances early detection and reduces human error in diagnosing cardiovascular conditions. Hybrid ML approaches improve prediction accuracy for heart disease. XGBoost and Random Forest have shown high accuracy rates in predicting CVDs. Swarm intelligence techniques are being integrated to enhance feature selection for better diagnosis. Guideline-Based Recommendations
Diagnosis
Utilize machine learning algorithms to analyze medical data for early identification of CVDs. Incorporate diverse datasets to improve the generalizability of predictive models. Management
Develop individualized treatment plans based on ML predictions of cardiovascular risk. Monitoring & Follow-up
Regularly assess the performance of ML models to ensure accuracy in predictions. Risks
Consider the impact of imbalanced datasets on prediction bias. Patient & Prescribing Data
Patients with risk factors for cardiovascular diseases.
Early intervention and personalized treatment plans based on predictive analytics can improve outcomes.
Clinical Best Practices
Implement AI-driven diagnostic tools to enhance early detection of CVD. Use ensemble methods to combine multiple ML algorithms for improved accuracy. References
