Life-inspired algorithms offer robust tools to tackle intricate classification challenges by applying principles derived from natural systems. This study presents the Camel-Support Vector Machine (SVM) predictive model aimed at categorizing patients having breast cancer into distinct subgroups attributed on clinical and pathological characteristics. The proposed model can also assess the likelihood of recurrence or non-recurrence events through an analysis of clinicopathological parameters and differentiate breast cancer patients by evaluating digitized images of tumor masses. The Camel-SVM predictive model was developed through a three-step process: In the first step, the Camel algorithm was adopted to optimize the SVM hyperparameters, aiming to achieve the most effective configuration. Secondly, the Camel algorithm was further applied for identifying a subset of pertinent features. In the third step, the selected features were used by the hyperparameter-tuned SVM classifier to identify breast cancer subgroups. The Camel-SVM hybrid model exhibited superior classification performance than four other established hybrid life-inspired models when evaluated across five different datasets of hospitals patients undergoing treatment for breast cancer. Thus, the newly created life-inspired hybrid model can be used as an alternate diagnostic tool that can identify the growing complexity of breast cancer for various patient cohorts.
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