Incorporating Repeating Temporal Association Rules in Na ïve Bayes Classifiers for Coronary Heart Disease Diagnosis

Publication date: Available online 16 March 2018 Source:Journal of Biomedical Informatics Author(s): Kalia Orphanou, Arianna Dagliati, Lucia Sacchi, Athena Stassopoulou, Elpida Keravnou, Riccardo Bellazzi In this paper, we develop a Naïve Bayes classification model integrated with temporal association rules (TARs). A temporal pattern mining algorithm is used to detect TARs by identifying the most frequent temporal relationships among the derived basic temporal abstractions (TA). We develop and compare three classifiers that use as features the most frequent TARs as follows: i) representing the most frequent TARs detected within the target class (’Disease = Present’), ii) representing the most frequent TARs from both classes (’Disease = Present’, ’Disease = Absent’), iii) representing the most frequent TARs, after removing the ones that are low-risk predictors for the disease. These classifiers incorporate the horizontal support of TARs, which defines the number of times that a particular temporal pattern is found in some patient’s record, as their features. All of the developed classifiers are applied for diagnosis of coronary heart disease (CHD) using a longitudinal dataset. We compare two ways of feature representation, using horizontal support or the mean duration of each TAR, on a single patient. The results obtained from this comparison show that the horizontal support representation outperforms the mean duration. The main effort of our research is...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research