Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Mahabala, C.

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    Diagnostic classification of undifferentiated fevers using artificial neural network
    (American Institute of Physics Inc. subs@aip.org, 2020) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.K.; Mahabala, C.; Dakappa, P.H.; Prasad, K.
    Accurate diagnosis of undifferentiated fever case at the earliest is a challenging effort, which needs extensive diagnostic tests. Prediction of undifferentiated fever cases at an early stage will help in diagnosing the disease in comparatively lesser time and more effectively. The aim of the present study was to apply Artificial Intelligence (AI) algorithm using temperature information for the prediction of major categories of diseases among undifferentiated fever cases. This was an observational study carried out in tertiary care hospital. Total of 103 patients were involved in the study and 24-hour continuous temperature recording was done. Analysis was done using Artificial Neural Network (ANN) model based on the temperature data of each patients and its statistical parameters. Temperature datasets were labeled with the help of experienced physicians. Levenberg Marquardt error back-propagation algorithm was used to train the network. A good relation was found between the target data set and output data set, purely based on the observed 24 hr continuous tympanic temperature of the patients. An accuracy of 98.1% was obtained from ANN prediction model. The study concluded that a single noninvasive temperature parameter is sufficient to predict the major categories of diseases using ANN algorithms, from the undifferentiated fever cases. © 2020 Author(s).
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    Leveraging Deep Learning for Fever Temperature Analysis and Pattern Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2024) Prabhakaran, A.; Sumam David, S.; Vijayasenan, D.; Mahabala, C.; Dakappa, P.
    Tympanic temperature is one of the most fundamental indicators for the diagnosis of diseases. Due to its importance, using patients' temperature data to aid in the diagnostic process would be beneficial. This work uses temperature data collected from various patients to classify diseases. We consider dengue, tuberculosis, and non-infectious and non-tubercular bacterial diseases. Extracting essential features from the temperature data is necessary so that the downstream layers only have to consider important features, not miscellaneous information. This feature extraction is done using two methods - Convolution Neural Networks and Autoencoders. We introduce three models for Explainable Temperature Analysis - ExTemp-Conv-SM, ExTemp-Conv-LG and ExTemp-Auto. We achieve a classification accuracy of 70% over these four disease classes. We also use explainable AI tools, like GradCAM, to identify distinguishing patterns in temperature fluctuations that can characterize diseases. We generate such patterns for all four diseases under consideration. We note that the patterns generated for dengue and tuberculosis match the findings in biological observation studies. We hope that the methods in this paper can be leveraged for other diseases and used to aid the diagnostic process. © 2024 IEEE.