Artifcial Bee Colony Algorithm with Cosine Similarity on Raspberry Pi Cluster for Enhancing Medical Data Classifcation
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Abstract
Machine learning plays a very important role in our daily lives. Machine learning is used to solve difficult problems encountered in many fields of study. Especially in medical diagnosis, machine learning is used to support patient diagnosis. In this research, the artificial bee colony approach, a well-known bio-inspired algorithm in the field of machine learning, is proposed to enhance the classification of cancer diagnosis data by combining it with cosine similarity measurements and parallel computing on Raspberry Pi clusters. The performance of the proposed method is validated and compared with other well-known algorithms against the Breast Cancer Wisconsin dataset taken from the UCI machine learning repository. Experimental results indicate that the proposed method achieves superior classification accuracy compared to existing approaches, yielding improvements of up to 6.45%. Furthermore, it can scale effectively to support large-scale medical data for disease diagnosis.
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