Medical pattern classification using a novel binary similarity approach based on an associative classifier.

Classification is a central task in machine learning, underpinning applications in domains such as finance, medicine, engineering, information technology, and biology. However, machine learning pattern classification can become a complex or even inexplicable task for current robust models due to the complexity of objective datasets, which is why there is a strong interest in achieving high classification performance. On the other hand, in particular cases, there is a need to achieve such performance while maintaining a certain level of explainability in the operation and decisions of classification algorithms, which can become complex. For this reason, an algorithm is proposed that is robust, simple, highly explainable, and applicable to datasets primarily in medicine with complex class imbalance. The main contribution of this research is a novel machine learning classification algorithm based on binary string similarity that is competitive, simple, interpretable, and transparent, as it is clear why a pattern is classified into a given class. Therefore, a comparative study of the performance of the best-known state-of-the-art classification algorithms and the proposed model is presented. The experimental results demonstrate the benefits of the proposal in this research work, which were validated through statistical hypothesis tests to assess significant performance differences.