Optimized machine learning mechanism for big data healthcare system to predict disease risk factor

Area of Science:

• Cardiovascular Health
• Artificial Intelligence in Medicine
• Predictive Analytics

Background:

• Heart disease prevalence is increasing due to modern lifestyle factors like stress and poor diet.
• Early identification of heart disease risk factors is crucial for timely intervention and improved patient outcomes.
• Existing predictive models often suffer from issues like suboptimal feature selection and overfitting.

Purpose of the Study:

• To introduce and implement a novel Deep Red Fox belief prediction system (DRFBPS) for predicting heart disease risk.
• To address limitations of traditional predictive methods in feature selection and model accuracy.
• To evaluate the efficacy of DRFBPS in healthcare analytics for early diagnosis and preventive care.

Main Methods:

• Data collection and preprocessing to ensure data quality.
• Feature selection using a red fox optimization algorithm.
• Prediction of heart disease risk factors using the developed DRFBPS model.
• Validation of DRFBPS performance using metrics like Accuracy, F score, Precision, AUC, Recall, and error rate.

Main Results:

• The DRFBPS model demonstrated accurate and reliable predictions for heart disease risk factors.
• Performance validation confirmed the model's effectiveness across multiple evaluation metrics.
• The study highlights DRFBPS as a practical tool for healthcare analytics.

Conclusions:

• DRFBPS offers a robust framework for predictive modeling in heart disease risk assessment.
• The system's applicability extends to clinical decision-making and remote patient monitoring.
• DRFBPS shows significant potential for enhancing early diagnosis and preventive strategies in cardiovascular health.