Healthcare organizations and Health Monitoring Systems generate large volumes of complex data, which offer the opportunity for innovative investigations in medical decision making. In this paper, we propose a beetle swarm optimization and adaptive neuro-fuzzy inference system (BSO-ANFIS) model for heart disease and multi-disease diagnosis. The main components of our analytics pipeline are the modified crow search algorithm, used for feature extraction, and an ANFIS classification model whose parameters are optimized by means of a BSO algorithm. The accuracy achieved in heart disease detection is $$99.1\%$$
99.1
%
with $$99.37\%$$
99.37
%
precision. In multi-disease classification, the accuracy achieved is $$96.08\%$$
96.08
%
with $$98.63\%$$
98.63
%
precision.
The results from both tasks prove the comparative advantage of the proposed BSO-ANFIS algorithm over the competitor models.
The term Cleaner Production (CP) for Production Companies is contemplated as influential to get sustainable production. CP mainly deals with three R's that is, reuse, reduce, and recycle. For software enterprise, the software reuse plays a pivotal role. Software reuse is a process of producing new products or software from the existing software by updating it. To extract useful information from the existing software data mining comes into light. The algorithms used for software reuse face issues related to maintenance cost, accuracy, and performance. Also, the currently used algorithm does not give accurate results on whether the component of software can be reused. Machine Learning gives the best results to predicate if the given software component is reusable or not. This paper introduces an integrated Random Forest and Gradient Boosting Machine Learning Algorithm (RFGBM) which test the reusability of the given software code considering the object‐oriented parameters such as cohesion, coupling, cyclomatic complexity, bugs, number of children, and depth inheritance tree. Further, the proposed algorithm is compared with J48, AdaBoostM1, LogitBoost, Part, One R, LMT, JRip, DecisionStump algorithms. Performance metrices like accuracy, error rate, Relative Absolute Error, and Mean Absolute Error are improved using RFGBM. This algorithm also utilizes data preprocessing with the help of an unsupervised filter to remove the missing value for efficiency improvement. Proposed algorithm outperforms existing in term of performance parameters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.