It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution. The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.

Under the background of “innovation and entrepreneurship,” how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF‐CSCA‐SVM, for predicting college students′ entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF‐CSCA‐SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students’ decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta‐heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.