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@ARTICLE{Panda_INFORMATICA_2021,
         author = {Panda, Debjani and Panda, Divyajyoti and Dash, Satya Ranjan and Parida, Shantipriya},
       projects = {Idiap},
          month = oct,
          title = {Extreme Learning Machines with feature selection using GA for effective prediction of fetal heart disease: A Novel Approach},
        journal = {Informatica},
         volume = {45},
         number = {3},
           year = {2021},
           issn = {0350-5596},
            url = {https://www.informatica.si/index.php/informatica/article/view/3223},
            doi = {https://doi.org/10.31449/inf.v45i3.3223},
       abstract = {Heart disease is considered to be the most life-threatening ailment in the entire world and has been a major concern of developing countries. Heart disease also affects the fetus, which can be detected by cardiotocography tests conducted on the mother during her pregnancy. This paper analyses the presence of heart disease in the foetus by optimizing the Extreme Learning Machine with a novel activation function (roots). The accuracy of predicting the heart condition of the foetus is measured and compared with other activation functions like sigmoid, Fourier, tan hyperbolic, and a user-defined function, called “roots”.

The best features from the Cardiotocography data set are selected by applying the Genetic Algorithm (GA). ELM with activation functions sigmoid, Fourier, tan hyperbolic, and roots (a novel function), have been measured and compared on accuracy, sensitivity, specificity, precision, F-score, area under the curve (AUC), and computation time metrics. The GA uses three types of regression: linear, lasso, and ridge, for cross-validation of the features. ELM with user-defined activation function shows comparable performance with sigmoid and hyperbolic tangent functions. Features selected from linear and lasso produce better results in ELM than those selected from the ridge.

It gives an accuracy of 96.45\% as compared to 94.56\% and 94.56\% respectively with the best features selected from both linear and lasso. The roots activation function also takes 2.50 seconds computation time versus 3.27 seconds and 2.67 seconds for sigmoid and hyperbolic tangent respectively and scores better on all other metrics in designing an efficient model to classify fetal heart disease.}
}