Faculty of Computer Science and Engineering

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The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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Author: search.filters.author.Mitrova, HristinaHas files: No

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    Item type:Publication,
    Models for Detecting Frauds in Medical Insurance
    (Springer, Cham, 2022-01-01)
    Mitrova, Hristina
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    Madevska Bogdanova, Ana
    Health insurance is important for many people, but unfortunately it is susceptible to frauds, therefore expenditures for covering the funds show exponential growth. The victims of this kind of scams are not only the institutions that provide the funds and treatments, but also are the ones who really need that help, except they have lost their priority due to a committed fraud. In order to rationally provide funds and minimize losses, there is a need for fraud detection systems. In this paper, this issue is considered as a binary classification problem, using data inherent in the nature of the field. The whole data science pipeline process is considered in order to elaborate our results that are higher than the published ones on the same problem: 0.95, 0.96 and 0.98 AUC scores with different models. The data is integrated from three interconnected databases, which are pre-processed and then their cross-section is undertaken. The dataset is unbalanced concerning the records of both classes, therefore certain balancing techniques are applied. Several models are built using traditional Machine Learning models, classifiers with Deep Neural Networks and ensemble algorithms and their performance is validated according to several evaluation metrics.
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    A Deep Learning Approach to Estimate SpO2 from PPG Signals
    (ACM, 2022-09-18)
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    Madevska Bogdanova, Ana
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    Mitrova, Hristina
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    Sidorenko, Marija
    ;
    Lehocki, Fedor
    Blood oxygen saturation level (SpO2) is one of the vital parameters determining the hemostability of a patient, besides heart rate (HR), respiratory rate (RR) and blood preasure (BP). In emergency situations with a high number of injured persons, during the second triage until arrival to a medical facility, continuously following the SpO2 level in real time is of outmost importance. Using a smart patch-like device attached to a injured’s chest that contains a Photoplethysmogram (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the smart patch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the SpO2 by extracting the set of features from the PPG signal utilizing Python toolkit HeartPy in order to model a Deep neural network regressor. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 - 100%. The best experimental results considering the SpO2 interval [83,95) were achieved with a PPG signal of 10 seconds length (MAPE 2.00% and 7.21% of big errors defined as absolute percentage errors (APE) equal or greater than 5).
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    Item type:Publication,
    Machine learning based SpO2 prediction from PPG signal's characteristics features
    (IEEE, 2022-06-22)
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    Mitrova, Hristina
    ;
    Madevska Bogdanova, Ana
    ;
    Lehocki, Fedor
    Continuous monitoring of blood oxygen saturation level (SpO2) during the second triage in the high casualty event and determining the hemostability of a patient/victim until arrival to a medical facility, is essential in emergency situations. Using a SmartPatch device attached to a victim's chest that contains a Photoplethysmogram Waveforms (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the SmartPatch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the Sp02 by extracting the set of features from the PPG signal utilizing two Python toolkits, HeartPy and Neurokit, in order to model the Machine learning predictors, using multiple regressors. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 – 100%. The best experimental results - MAE (1.45), MSE (3.85), RMSE (1.96) and RMSLE (0.02) scores are achieved with the Random Forest regressor in the experiment with 7 features extracted from the both toolkits.