To address the fast convergence that leads to a tendency to local optimal solutions of the sparrow search algorithm SSA when solving problems, this study proposes a sparrow search algorithm incorporating multi-strategy improvement (LCSSA). Firstly, the ability of global searching and to jump out of local optimal solutions is enhanced by introducing nonlinear decreasing weights and Levy flight strategy to jointly improve the discoverer position updating formula. Secondly, Cauchy mutation is introduced to update the positions of the followers, that is, the optimal solution is updated and perturbed. The study selects four comparison algorithms on 12 benchmark functions for comparative experiments. The experimental results show that the improved algorithm has achieved effective improvement in convergence speed and stability. In disease prediction, LCSSA has a good performance in four chronic disease datasets, showing higher prediction accuracy compared with selected algorithms.