Summary
Background
Clinical time-series data acquired from electronic health records (EHR) are liable
to temporal complexities such as irregular observations, missing values and time constrained
attributes that make the knowledge discovery process challenging.
Objective
This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework
that handles these complexities and builds an effective clinical decision-making system.
TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal
classification.
Method
In TDA, a time-series forecasting model is constructed by adopting an improved double
exponential smoothing method. The forecasting model is used in missing value imputation
and temporal pattern extraction. The relevant attributes are selected using a temporal
pattern based rough set approach. In temporal classification, a classification model
is built with the selected attributes using a temporal pattern induced neuro-fuzzy
classifier.
Result
For experimentation, this work uses two clinical time series dataset of hepatitis
and thrombosis patients. The experimental result shows that with the proposed TRiNF
framework, there is a significant reduction in the error rate, thereby obtaining the
classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis
dataset.
Conclusion
The obtained classification results prove the efficiency of the proposed framework
in terms of its improved classification accuracy.
Keywords
Clinical time-series data - temporal mining - temporal data acquisition - rough set
- neuro-fuzzy
