Support vector machine prediction model based on fractional particle swarm algorithm

The support vector machine algorithm is widely used to solve nonlinear classification problems with its good generalization ability. This paper mainly explores the parameter optimization problem of the algorithm in detail. The method is proposing an improved fractional particle swarm algorithm, that is, set a linear decrease strategy for the inertia weight, and randomly adopt the single point mutation operation of the genetic algorithm during the particle update process. The improved particle swarm algorithm is utilized to optimize the parameters of the support vector machine to build a heart disease prediction model. The new algorithm can effectively avoid falling into the local optimal solution. The convergence speed, stability, and accuracy of the algorithm have been significantly improved, and further improving the ability to find the global optimal solution. The simulation experiment also proved the improvement of the diagnostic efficiency and accuracy of the predictive model. It significantly reduces the diagnosis errors and makes the prediction results have certain practical significance.