Optimized machine learning mechanism for big data healthcare system to predict disease risk factor
Venkata Nagaraju Thatha, Silpa Chalichalamala, Udayaraju Pamula, D. Pramodh Krishna, Manjunath Chinthakunta, Srihari Varma Mantena, Shariff Vahiduddin, Ramesh Vatambeti
Abstract
Heart disease is becoming more and more common in modern society because of factors like stress, inadequate diets, etc. Early identification of heart disease risk factors is essential as it allows for treatment plans that may reduce the risk of severe consequences and enhance patient outcomes. Predictive methods have been used to estimate the risk factor, but they often have drawbacks such as improper feature selection, overfitting, etc. To overcome this, a novel Deep Red Fox belief prediction system (DRFBPS) has been introduced and implemented in Python software. Initially, the data was collected and preprocessed to enhance its quality, and the relevant features were selected using red fox optimization. The selected features analyze the risk factors, and DRFBPS makes the prediction. The effectiveness of the DRFBPS model is validated using Accuracy, F score, Precision, AUC, Recall, and error rate. The findings demonstrate the use of DRFBPS as a practical tool in healthcare analytics by showing the rate at which it produces accurate and reliable predictions. Additionally, its application in healthcare systems, including clinical decisions and remote patient monitoring, proves its real-world applicability in enhancing early diagnosis and preventive care measures. The results prove DRFBPS to be a potential tool in healthcare analytics, providing a strong framework for predictive modeling in heart disease risk prediction.