Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project

AimsAs cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach.Methods and resultsWe performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration.ConclusionsUsing a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD

Unbiasing the Estimation of Chlorophyll from Hyperspectral Images: A Benchmark Dataset, Validation Procedure and Baseline Results

Recent advancements in hyperspectral remote sensing bring exciting opportunities for various domains. Precision agriculture is one of the most widely-researched examples here, as it can benefit from the non-invasiveness and enormous scalability of the Earth observation solutions. In this paper, we focus on estimating the chlorophyll level in leaves using hyperspectral images—capturing this information may help farmers optimize their agricultural practices and is pivotal in planning the plants’ treatment procedures. Although there are machine learning algorithms for this task, they are often validated over private datasets; therefore, their performance and generalization capabilities are virtually impossible to compare. We tackle this issue and introduce an open dataset including the hyperspectral and in situ ground-truth data, together with a validation procedure which is suggested to follow while investigating the emerging approaches for chlorophyll analysis with the use of our dataset. The experiments not only provided the solid baseline results obtained using 15 machine learning models over the introduced training-test dataset splits but also showed that it is possible to substantially improve the capabilities of the basic data-driven models. We believe that our work can become an important step toward standardizing the way the community validates algorithms for estimating chlorophyll-related parameters, and may be pivotal in consolidating the state of the art in the field by providing a clear and fair way of comparing new techniques over real data

Digital Image of Analog Reality – Laboratory Practice in Computer Science

Zagadnienie cyfrowej jakości dźwięku i obrazu jest związane przede wszystkim z parametrami przetwarzania analogowo-cyfrowego wymagającego czytelnej ekspozycji. W artykule zaprezentowano opis ćwiczenia laboratoryjnego możliwego do wykonania w praktyce szkoły ponadpodstawowej, mającego na celu ocenę wpływu częstotliwości próbkowania na jakość dźwięku.The issue of digital sound and picture quality is associated primarily with the parameters of the analog conversion, which requires clear exposure. The article presents a description of laboratory exercises that can be performed practically in secondary school. Aim of the exercise is the evaluation of the impact of the sampling frequency for audio quality

Selected problems of acoustics on the basis of sound attenuation

Jednym z problemów poruszanych w akustyce jest zjawisko tłumienia dźwięków. W artykule zagadnienie to zostało przedstawione na przykładzie zmian w strukturze dźwięku podczas transmisji przez powłoki brzuszne kobiety w ciąży. Zjawisko to może być zamodelowane filtrem dolnoprzepustowym pierwszego rządu. W artykule wykazano zmiany w charakterystyce amplitudowej i fazowej dźwięku docierającego do ucha płodu oraz wskazano na znaczące zmiany w jego barwie.One of the problems of acoustics is the sound attenuation. In this paper this problem was described on the basis of changes in sound structure during transmission through pregnant women’s abdominal wall. This phenomenon can be modeled with first order low-pass filter. In this paper, changes in amplitude and phase characteristics of the sound, which reach to the fetus ear, and changes in sound timbre have been shown

Proposition of validation of EduMATRIX as the educational tool for supporting the teaching process of mathematics in early education classes

Liczne propozycje narzędzi wykorzystywanych w procesie dydaktycznym mają na celu wspomaganie rozwoju i umiejętności uczniów. Wykonany z naturalnych materiałów zestaw Edu-MATRIX stanowi ciekawą alternatywę dla dostępnych na rynku propozycji. Metoda walidacji EduMATRIX opiera się na projekcie eksperymentu z grupą kontrolną. Zebrane dane posłużą do oceny wpływu zestawu EduMATRIX na poprawność oraz czas rozwiązywania zadań matematycz-nych opracowanych w oparciu o standaryzowany materiał. Analizie poddano również opinie dzieci odnoszące się do ich samopoczucia w trakcie zajęć oraz satysfakcji z wykonywanych zadań, a także oceny nauczycieli dotyczące postępów w nauce matematyki u dzieci uczestniczących w badaniu.Many suggestions of tools, which are used in the teaching process, should support the devel-opment and abilities of pupils. EduMATRIX set, which is made of natural materials, is an interesting alternative for other proposals available on the market. Validation process of this set is based on experimental design with a control group. The collected data will be used to assess the impact of EduMATRIX on the correctness and the time of solving mathematical tasks, which are designe

Support for learning programming in early education using EduMATRIX

Obecnie bardzo duży nacisk w procesie edukacji kładziony jest na rozwijanie umiejętności logicznego i abstrakcyjnego myślenia, co jest niezbędne do nauki programowania. Powszechnie dostępne są systemy, które pomagają oswoić się z tym zagadnieniem już od najmłodszych lat. W niniejszej pracy przedstawiona została alternatywa dla takich aplikacji – bloczki EduMATRIX. Ich głównym atutem jest nauczanie zagadnień związanych z programowaniem bez konieczności przebywania przed ekranem komputera. Ponadto zaproponowano metodę walidacji użyteczności EduMATRIX oraz innych dostępnych pomocy dydaktycznych, która pozwoli na wskazanie skutecznej formy nauki dla młodych użytkowników.Currently, the development of the skills of logical and abstract thinking is empahasize in the edu-cation process. This is essential for learning programming. Systems that help to be familiar with this issue since an early age are widely available. In this paper an alternative for such applications is presented – EduMATRIX. Its main advantage is the teaching of programming without the need to stay in front of a computer screen. Moreover, a validation method of EduMATRIX and other available teaching aids, which will identify effective form of learning for young users, was proposed

Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project

Abstract Background Diabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with or without MASLD. This study aims to develop machine learning (ML) models for assessing the risk of the HF occurrence in patients with DM with and without MASLD. Research design and methods In the Silesia Diabetes-Heart Project (NCT05626413), patients with DM with and without MASLD were analyzed to identify the most important HF risk factors with the use of a ML approach. The multiple logistic regression (MLR) classifier exploiting the most discriminative patient’s parameters selected by the χ2 test following the Monte Carlo strategy was implemented. The classification capabilities of the ML models were quantified using sensitivity, specificity, and the percentage of correctly classified (CC) high- and low-risk patients. Results We studied 2000 patients with DM (mean age 58.85 ± SD 17.37 years; 48% women). In the feature selection process, we identified 5 parameters: age, type of DM, atrial fibrillation (AF), hyperuricemia and estimated glomerular filtration rate (eGFR). In the case of MASLD( +) patients, the same criterion was met by 3 features: AF, hyperuricemia and eGFR, and for MASLD(−) patients, by 2 features: age and eGFR. Amongst all patients, sensitivity and specificity were 0.81 and 0.70, respectively, with the area under the receiver operating curve (AUC) of 0.84 (95% CI 0.82–0.86). Conclusion A ML approach demonstrated high performance in identifying HF in patients with DM independently of their MASLD status, as well as both in patients with and without MASLD based on easy-to-obtain patient parameters. Graphical Abstrac

Relationship of vitamin D deficiency with cardiovascular disease and glycemic control in patients with type 2 diabetes mellitus: the Silesia Diabetes-Heart Project

IntroductionVitamin D (VD) has a pleiotropic effect on many health‑related aspects, yet the results of studies regarding vitamin D deficiency (VDD) and both glycemic control and cardiovascular disease (CVD) are conflicting.ObjectiveThe aim of this work was to determine the prevalence of VDD and its associations with CVD and glycemic control among patients with type 2 diabetes mellitus (T2DM).Patients and methodsThis was an observational study in T2DM patients recruited at the diabetology clinic in Zabrze, Poland (April-September 2019 and April-September 2020). The presence of CVD was determined based on medical records. Blood biochemical parameters, densitometry, and carotid artery ultrasound examination were performed. Control of diabetes was assessed based on glycated hemoglobin A1c (HbA1c) levels. A serum VD level below 20 ng/ml was considered as VDD.ResultsThe prevalence of VDD in 197 patients was 36%. CVD was evident in 27% of the patients with VDD and in 33% of the patients with VD within the normal range (vitamin D sufficiency [VDS]) (P = 0.34). The difference between the groups regarding diabetes control was insignificant (P = 0.05), as for the VDD patients the median value (interquartile range) of HbA1c was 7.5% (6.93%-7.9%), and for VDS patients it was 7.5% (6.56%-7.5%). The VDD patients were more often treated with sodium‑glucose cotransporter‑2 inhibitors (SGLT‑2is) (44% vs 25%; P = 0.01).ConclusionsAbout one‑third of the patients showed VDD. The VDD and VDS groups did not differ in terms of CVD occurrence and the difference in glycemic control was insignificant. The patients with VDD were more often treated with SGLT‑2is, which requires further investigation

Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project

We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015-2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months. An end-to-end ML technique which exploits the neighborhood component analysis for elaborating discriminative predictors, followed by a hybrid sampling/boosting classification algorithm, multiple logistic regression (MLR), or unsupervised hierarchical clustering was proposed. In 1735 patients with diabetes (53% female), there were 150 (8.65%) ones with a new CV event in the follow-up. Twelve most discriminative patients' parameters included coronary artery disease, heart failure, peripheral artery disease, stroke, diabetic foot disease, chronic kidney disease, eosinophil count, serum potassium level, and being treated with clopidogrel, heparin, proton pump inhibitor, and loop diuretic. Utilizing those variables resulted in the area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95% Confidence Interval [CI] 0.56-0.68, P < 0.01) to 0.72 (95% CI 0.66-0.77, P < 0.01) across 5 nonoverlapping test folds, whereas MLR correctly determined 111/150 (74.00%) high-risk patients, and 989/1585 (62.40%) low-risk patients, resulting in 1100/1735 (63.40%) correctly classified patients (AUC: 0.72, 95% CI 0.66-0.77). ML algorithms can identify patients with diabetes at a high risk of new CV events based on a small number of interpretable and easy-to-obtain patients' parameters