Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm.

Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices.

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms.

Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural design methods. However, the spatial variability of soil properties and the nature of test protocols, the laboratory determination of Mr has become inexpedient. This paper aims to design an accurate soft computing technique for the prediction of Mr of subgrade soils using the hybrid least square support vector machine (LSSVM) approaches. Six swarm intelligence algorithms, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), symbiotic organisms search (SOS), salp swarm algorithm (SSA), slime mould algorithm (SMA), and Harris hawks optimization (HHO) have been applied and compared to optimize the LSSVM parameters. For this purpose, a literature dataset (891 datasets) of different types of soils has been used to design and evaluate the proposed models. The input variables in all of the proposed models included confining stress, deviator stress, unconfined compressive strength, degree of soil saturation, soil moisture content, optimum moisture content, plasticity index, liquid limit, and percent of soil particles (P #200). The accuracy of the proposed models was assessed by comparing the predicted with the observed of Mr values with respect to different statistical analyses, i.e., root means square error (RMSE) and determination coefficient (R2). For modeling the Mr of subgrade soils, percent passing No. 200 sieve, optimum moisture content, and unconfined compressive strength were found to be the most significant variables. It is observed that the performance of LSSVM-GWO, LSSVM-SOS, and LSSVM-SSA outperforms other models in predicting accurate values of Mr. The (RMSE and R2) of the LSSVM-GWO, LSSVM-SSA, and LSSVM-SOS are (6.79 MPa and 0.940), (6.78 MPa and 0.940), and (6.72 MPa and 0.942), respectively, and hence, LSSVM-SOS can be used for high estimating accuracy of Mr of subgrade soils.