Lupus nephritis diagnosis using enhanced moth flame algorithm with support vector machines

Systemic lupus erythematosus is a chronic autoimmune disease that affects the kidney in most patients. Lupus nephritis (LN) is divided into six categories by the International Society of Nephrology/Renal Pathology Society (ISN/RPS). The purpose of this research is to build a framework for discriminating between ISN/RPS pure class V (MLN) and classes III +/- V or IV +/- V (PLN) using real clinical data. The framework is developed by merging a hybrid stochastic optimizer, moth-flame algorithm (HMFO), with a support vector machine (SVM), dubbed HMFO-SVM. The HMFO is constructed by enhancing the original moth-flame algorithm (MFO) with a beeforaging learning operator, which guarantees that the algorithm speeds convergence and departs from the local optimum. The HMFO is used to optimize parameters and select features simultaneously for SVM on clinical SLE data. On 23 benchmark tests, the suggested HMFO method is validated. Finally, clinical data from LN patients are analyzed to determine the efficacy of HMFO-SVM over other SVM rivals. The statistical findings indicate that all measures have predictive capabilities and that the suggested HMFO-SVM is more stable for analyzing systemic LN. HMFO-SVM may be used to analyze LN as a feasible computer-assisted technique.

Automated summary

This research aims to build a framework for discriminating between different types of lupus nephritis using real clinical data. By combining a hybrid stochastic optimizer moth-flame algorithm with support vector machine, a more stable and effective computer-assisted technique for analyzing systemic lupus erythematosus nephritis is developed.