TY - JOUR A1 - Chumachenko, Dmytro A1 - Butkevych, Mykola A1 - Lode, Daniel A1 - Frohme, Marcus A1 - Schmailzl, Kurt J. G. A1 - Nechyporenko, Alina T1 - Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data JF - Sensors N2 - Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks. KW - myocardial infraction KW - heart rate variability KW - 10-second heart rate variability KW - diagnostics KW - machine learning KW - k-nearest neighbor classifier KW - radial basis function KW - decision tree KW - random forest Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16521 SN - 1424-8220 VL - 22 IS - 18 PB - MDPI ER - TY - JOUR A1 - Marcos-Zambrano, Laura Judith A1 - López-Molina, Víctor Manuel A1 - Bakir-Gungor, Burcu A1 - Frohme, Marcus A1 - Karaduzovic-Hadziabdic, Kanita A1 - Klammsteiner, Thomas A1 - Ibrahimi, Eliana A1 - Lahti, Leo A1 - Loncar-Turukalo, Tatjana A1 - Dhamo, Xhilda A1 - Simeon, Andrea A1 - Nechyporenko, Alina A1 - Pio, Gianvito A1 - Przymus, Piotr A1 - Sampri, Alexia A1 - Trajkovik, Vladimir A1 - Lacruz-Pleguezuelos, Blanca A1 - Aasmets, Oliver A1 - Araujo, Ricardo A1 - Anagnostopoulos, Ioannis A1 - Aydemir, Önder A1 - Berland, Magali A1 - Calle, M. Luz A1 - Ceci, Michelangelo A1 - Duman, Hatice A1 - Gündoğdu, Aycan A1 - Havulinna, Aki S. A1 - Kaka Bra, Kardokh Hama Najib A1 - Kalluci, Eglantina A1 - Karav, Sercan A1 - Lode, Daniel A1 - Lopes, Marta B. A1 - May, Patrick A1 - Nap, Bram A1 - Nedyalkova, Miroslava A1 - Paciência, Inês A1 - Pasic, Lejla A1 - Pujolassos, Meritxell A1 - Shigdel, Rajesh A1 - Susín, Antonio A1 - Thiele, Ines A1 - Truică, Ciprian-Octavian A1 - Wilmes, Paul A1 - Yilmaz, Ercument A1 - Yousef, Malik A1 - Claesson, Marcus Joakim A1 - Truu, Jaak A1 - Carrillo de Santa Pau, Enrique T1 - A toolbox of machine learning software to support microbiome analysis JF - Frontiers in Microbiology N2 - The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis. KW - microbiome KW - machine learning KW - software KW - feature generation KW - feature analysis KW - data integration KW - microbial gene prediction KW - microbial metabolic modeling Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18271 UR - https://www.frontiersin.org/articles/10.3389/fmicb.2023.1250806/ SN - 1664-302X VL - 14 PB - Frontiers ER -