A bio-statistical mining approach for classifying multivariate clinical time series data observed at irregular intervals. (15th July 2017) - Nancy, Jane Y.; Khanna, Nehemiah H.; Kannan, Arputharaj
Highlights: Temporal mining framework for classifying unevenly spaced clinical time series data. Framework provides: temporal pre-processing, attribute selection and classification. Fuzzy Inference Double Exponential Smoothing method is proposed for pre-processing. Temporal pattern based tolerance rough set algorithm is presented for attribute selection. Decision tree classifier with temporal pattern induced gain ratio is used for classification. Abstract: In medical information system, the data that describe patient health records are often time stamped. These data are liable to complexities such as missing data, observations at irregular time intervals and large attribute set. Due to these complexities, mining in clinical time-series data, remains a challenging area of research. This paper proposes a bio-statistical mining framework, named statistical tolerance rough set induced decision tree (STRiD), which handles these complexities and builds an effective classification model. The constructed model is used in developing a clinical decision support system (CDSS) to assist the physician in clinical diagnosis. The STRiD framework provides the following functionalities namely temporal pre-processing, attribute selection and classification. In temporal pre-processing, an enhanced fuzzy-inference based double exponential smoothing method is presented to impute the missing values and to derive the temporal patterns for each attribute. In attribute selection, relevant attributes are selected using the tolerance rough set. A classification model is constructed with the selected attributes using temporal pattern induced decision tree classifier. For experimentation, this work uses clinical time series datasets of hepatitis and thrombosis patients. The constructed classification model has proven the effectiveness of the proposed framework with a classification accuracy of 91.5% for hepatitis and 90.65% for thrombosis.