Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: A decade follow-up in a Middle East prospective cohort study

Objective: The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. Design: Prospective cohort study. Setting: Tehran Lipid and Glucose Study (TLGS). Methods: A total of 6647 participants (43.4 men) aged >20 years, without T2D at baselines ((1999- 2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. Primary outcome measure: T2D was primary outcome which defined if fasting plasma glucose (FPG) was �7 mmol/L or if the 2h-PCPG was �11.1 mmol/L or if the participant was taking antidiabetic medication. Results: During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78 and 0.75) and (78 and 0.78) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-toheight ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG�4.9 mmol/L and 2h-PCPG�7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG�5.2 mmol/L and WHtR�0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. Conclusions: Our study emphasises the utility of DT for exploring interactions between predictor variables

Ventilation prediction for an industrial cement raw ball mill by bnn—a “conscious lab” approach

In cement mills, ventilation is a critical key for maintaining temperature and material transportation. However, relationships between operational variables and ventilation factors for an industrial cement ball mill were not addressed until today. This investigation is going to fill this gap based on a newly developed concept named “conscious laboratory (CL)”. For constructing the CL, a boosted neural network (BNN), as a recently developed comprehensive artificial intelligence model, was applied through over 35 different variables, with more than 2000 records monitored for an industrial cement ball mill. BNN could assess multivariable nonlinear relationships among this vast dataset, and indicated mill outlet pressure and the ampere of the separator fan had the highest rank for the ventilation prediction. BNN could accurately model ventilation factors based on the operational variables with a root mean square error (RMSE) of 0.6. BNN showed a lower error than other traditional machine learning models (RMSE: random forest 0.71, support vector regression: 0.76). Since improving the milling efficiency has an essential role in machine development and energy utilization, these results can open a new window to the optimal designing of comminution units for the material technologies. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Učení intervalově ohodnocených fuzzy kognitivních map algoritmem PSO pro predikci abnormálních akciových výnosů

Stock return prediction is considered a challenging task in financial domain. The existence of inherent noise and volatility in daily stock price returns requires a highly complex prediction system. Generalizations of fuzzy systems have shown promising results for this task owing to their ability to handle strong uncertainty in dynamic financial markets. Moreover, financial variables are usually in difficult to interpret causal relationships. To overcome these problems, here we propose an interval-valued fuzzy cognitive map with PSO algorithm learning. This system is suitable for modelling complex nonlinear problems through causal reasoning. As the inputs of the system, we combine causally connected financial indicators and linguistic variables extracted from management discussion in annual reports. Here we show that the proposed method is effective for predicting abnormal stock return. In addition, we demonstrate that this method outperforms fuzzy cognitive maps and adaptive neuro-fuzzy rule-based systems with PSO learning.Predikce výnosů akcií je v oblasti financí považována za náročnou úlohu. Existence inherentního šumu a kolísání denních výnosů cen akcií vyžaduje velmi komplexní predikční systém. Generalizace fuzzy systémů ukazují slibné výsledky vzhledem k jejich schopnosti modelovat silnou nejistotu na dynamických finančních trzích. Finanční proměnné jsou navíc obvykle v obtížně interpretovatelných kauzálních vztazích. Abychom překonali tyto problémy, navrhujeme zde intervalovou fuzzy kognitivní mapu s učením pomocí PSO algoritmu. Tento systém je vhodný pro modelování komplexních nelineárních problémů pomocí kauzálního usuzování. Jako vstupy systému spojujeme kauzálně propojené finanční ukazatele a jazykové proměnné, které jsou získávány z diskuse managementu ve výročních zprávách. Ukazujeme, že navrhovaná metoda je účinná pro predikci abnormálního výnosu akcií. Navíc prokazujeme, že tato metoda překonává fuzzy kognitivní mapy a adaptivní systémy založené na neuro-fuzzy pravidlech s PSO učením