@article{MAKHILLJEAS2017122114958,
    title = {A HCC Recurrence Prediction in Multiple Time Series Clinical Data with Merging
Statistical Measures of Advanced Frequency Spectrum of Time Series Features},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {21},
    pages = {5473-5477},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.5473.5477},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.5473.5477},
    author = {P. and},
    keywords = {Clinical data mining,Hepatocellular carcinoma,Curvelet transform,Firefly algorithm,support vector machine,optimization},
    abstract = {Now a days clinical data mining is used for clinicians in order to provide diagnosis, therapy and
prognosis of different diseases. The accuracy of clinical-outcome prediction has been increased by using
multiple measurements which are gathered from different time period and dataset. The multiple measurements
are merged by using merging algorithm and the distribution of data is determined by statistical measurement.
Then those data are given to the classifier for predicting the recurrence and non-recurrence of Hepatocellular
Carcinoma (HCC) patients. In this study, an improved multiple time series clinical data processing is proposed.
In the proposed approach, an additional measurement feature according to the frequency interval of features
is included for reducing the error rate of classifier and increasing the prediction rate. The frequency based
measurement feature is computed based on curvelet transform. Then, the optimal features are selected based
on the Firefly optimization algorithm for reducing the classification overhead. The selected optimal features are
learned by using the Support Vector Machine (SVM) classifier for predicting the patients with HCC disease and
patients without HCC effectively. Finally, the experimental results prove that the proposed method has better
performance than other classification methods.}
    }