Optimizing bioinformatics applications: a novel approach with human protein data and data mining techniques

Abstract

Biomedicine plays a crucial role in medical research, particularly in optimizing techniques for disease prediction. However, selecting effective optimization methods and managing vast amounts of medical data pose significant challenges. This study introduces a novel optimization technique, integrated bioinformatics optimization model (IBOM) for disease diagnosis, incorporating data mining to efficiently store large datasets for future analysis. Various optimization algorithms, such as whale optimization algorithm (WOA), multi-verse optimization (MVO), genetic algorithm (GA), and ant colony optimization (ACO), were compared with the proposed method. The evaluation focused on metrics like accuracy, specificity, sensitivity, precision, F-score, error, receiver operating characteristic (ROC), and false positive rate (FPR) using 5-fold cross-validation. Results indicated that the 5-fold cross-validation method achieved superior performance with metrics: 98.61% accuracy, 96.59% specificity, 88.63% sensitivity, 99.30% precision, 92.31% F-score, 10.80% error, 92.61% ROC, and a 3.00% FPR. This method was found to be the most effective, achieving an accuracy of 0.92 in disease diagnosis compared to other optimization techniques.