Medical datasets frequently include vast feature sets with numerous features that are related to one another. As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. Feature selection (FS) is a major step in classification and also in reducing the dimension. This study attempts a novel Binary Multi-objective Chimp Optimization Algorithm (BMOChOA) with dual archive and k-nearest neighbors (KNN) classifier for mining relevant aspects from medical data. In this research, 12 versions of BMOChOA are implemented based on the group information and types of chaotic functions used. The best Pareto front obtained from suggested BMOChOA variations is compared with three benchmark multi-objective FS methods by taking 14 popular medical datasets of variable dimensions. By analyzing the experimental outputs using four multi-objective performance evaluators, it is found that the proposed FS method is superior in finding the best trade-off between the two objective functions: the number of features and classification performance.

This work aims to discover the relevant factors to predict the health condition of COVID-19 patients by employing a fresh and enhanced binary multi-objective hybrid filter-wrapper chimp optimization (EBMOChOA-FW) based feature selection (FS) approach. FS is a preprocessing approach that has been highly fruitful in medical applications, as it not only reduces dimensionality but also allows us to understand the origins of an illness. Wrappers are computationally expensive but have excellent classification performance, whereas filters are recognized as quick techniques, although they are less accurate. This study presents an advanced binary multi-objective chimp optimization method based on the hybridization of filter and wrapper for the FS task using two archives. In exceptional instances, the initial ChOA version becomes stuck at the local optima. As a result, a novel ChOA termed EBMOChOA is developed here by integrating the Harris Hawk Optimization (HHO) into the original ChOA to improve the optimizer’s search capabilities and broaden the usage sectors. The location change step in the ChOA optimizer is separated into three parts: modifying the population using HHO to produce an HHO-based population; creating hybrid entities according to HHO-based and ChOA-based individuals; and altering the search agent in the light of greedy technique and ChOA’s tools. The effectiveness of the EBMOChOA-FW is proven by comparing it to five other well-known algorithms on nine different benchmark datasets. Then its strengths are applied to three real-world COVID-19 datasets to predict the health condition of COVID-19 patients.

Software defect prediction identifies defect-prone modules before testing, reducing costs and development time. Machine learning techniques are widely used, but high-dimensional datasets often degrade classification accuracy due to irrelevant features. To address this, effective feature selection is essential but remains an NP-hard challenge best tackled with heuristic algorithms. This study introduces a binary, multi-objective starfish optimizer for optimal feature selection, balancing feature reduction and classification performance. A Choquet fuzzy integral-based ensemble classifier further enhances prediction reliability by aggregating multiple classifiers. The approach was validated on five NASA datasets, demonstrating superior performance over traditional classifiers. Key software metrics—such as design complexity, operators and operands count, lines of code, and numbers of branches—were found to significantly influence defect prediction. The results show that the proposed method improves classification performance by 1% to 13% while retaining only 33% to 57% of the original feature set, offering a reliable and interpretable solution for software defect prediction. This approach holds strong potential for broader, high-dimensional classification tasks.