Abstract: A variances handling method for clinical pathway was proposed, which is based on TakagiSugeno (TS) fuzzy neural networks (FNNs) with random cooperative decomposing particle swarm optimization (RCDPSO). During the process of cooperative coevolution with random execution sequence, a decomposing algorithm was adopted for the particles with the highest performance, and crossover and mutations were adopted for the particles with the worst performance. Therefore, it not only ensures the convergence rate, but also improves performance of the algorithm in global search. Moreover, the variation disturbing mechanism was introduced to strengthen the diversity of population and avoid plunging into local optimum. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy was used to validate the proposed method. The result demonstrates that TS FNNs based on the RCDPSO achieves superior performance in prediction and robustness to TS FNNs based on other algorithms, which makes variances handling of clinical pathway more effective.