SeqClin: Pattern-Based Analysis and Classification of Clinical Datasets

Accurate analysis and classification of clinical datasets are crucial for understanding disease patterns, identifying risk factors and devising targeted interventions that ultimately contribute towards effective healthcare systems and improved patient outcomes. However, existing analysis and classification methods often fall short of effectively capturing complex sequential relationships within patient data and have limited interpretability. To overcome these challenges, we introduce SeqClin, a novel approach that utilizes frequent pattern mining to obtain valuable sequential information from clinical datasets. SeqClin first transforms clinical datasets into an appropriate format. Then, it employs sequential pattern mining algorithms to find frequent sequential patterns as well as rules of patient features in the datasets. These identified feature patterns and their respective values are then used for classification/detection. The performance of SeqClin is evaluated on four clinical datasets, where six classification models and evaluation metrics are employed for a comprehensive assessment. The obtained results show that the proposed approach surpassed previous approaches, with the extracted patterns and rules providing valuable insights into the key patient features and their values in clinical datasets.