COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin, Dilber Uzun; Onakpojeruo, Efe Precious; Duwa, Basil Bartholomew; Usman, A. G.; Abba, S.I.; Uzun, Berna

doi:10.3390/diagnostics13071264

Cited by 6 publications

(5 citation statements)

References 35 publications

(45 reference statements)

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“…One popular method for RLR is called the “M-estimator,″ which is based on minimizing a weighted sum of squared residuals. The weights are chosen to downweight the influence of outliers so that they have less impact on the final model …”

Section: Methodsmentioning

confidence: 99%

“…The weights are chosen to downweight the influence of outliers so that they have less impact on the final model. 32 …”

Section: Methodsmentioning

confidence: 99%

“…The weights are chosen to downweight the influence of outliers so that they have less impact on the final model. 32 Thus, it also examines the interplay between variables and describes their relationship by modifying only one independent variable. Correlations between n regressor factors and dependent variable y are shown in eq 2.…”

Section: Regression Tree (Rt)mentioning

confidence: 99%

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Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions

Usman,

Tanimu,

Abba

et al. 2023

ACS Omega

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The prediction of the yields of light olefins in the direct conversion of crude oil to chemicals requires the development of a robust model that represents the crude-tochemical conversion processes. This study utilizes artificial intelligence (AI) and machine learning algorithms to develop single and ensemble learning models that predict the yields of ethylene and propylene. Four single-model AI techniques and four ensemble paradigms were developed using experimental data derived from the catalytic cracking experiments of various crude oil fractions in the advanced catalyst evaluation reactor unit. The temperature, feed type, feed conversion, total gas, dry gas, and coke were used as independent variables. Correlation matrix analyses were conducted to filter the input combinations into three different classes (M1, M2, and M3) based on the relationship between dependent and independent variables, and three performance metrics comprising the coefficient of determination (R 2 ), Pearson correlation coefficient (PCC), and mean square error (MSE) were used to evaluate the prediction performance of the developed models in both calibration and validations stages. All four single models have very low R 2 and PCC values (as low as 0.07) and very high MSE values (up to 4.92 wt %) for M1 and M2 in both calibration and validation phases. However, the ensemble ML models show R 2 and PCC values of 0.99−1 and an MSE value of 0.01 wt % for M1, M2, and M3 input combinations. Therefore, ensemble paradigms improve the performance accuracy of single models by up to 58 and 62% in the calibration and validation phases, respectively. The ensemble paradigms predict with high accuracy the yield of ethylene and propylene in the catalytic cracking of crude oil and its fractions.

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Section: Methodsmentioning

confidence: 99%

“…The weights are chosen to downweight the influence of outliers so that they have less impact on the final model. 32 …”

Section: Methodsmentioning

confidence: 99%

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Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions

Usman,

Tanimu,

Abba

et al. 2023

ACS Omega

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“…The approach works for situations when the interaction among parameters is primarily linear, since it predicts the output by assuming a linear arrangement of inputs. As opposed to fuzzy logic or fuzzy inference systems, which use membership functions, MLR uses statistical concepts to estimate the coefficients and generate predictions depending on the inputs [29].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest

Uzun Ozsahin,

Duwa,

Ozsahin

et al. 2024

Diagnostics

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Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.

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“…The variable selection problem can be defined as the combinatorial optimization problem, which proposes to reduce the number of variables and remove inappropriate data for the improvement of the interpretability and validity of the model. The selection of optimal subset variables with penalized methods depends on the optimal selection of hyper-tuning parameters [16], [17]. The choice of hypertuning parameters has always been a challenge in regularized regression models, particularly for censored data.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Elastic Net Based on Modified PSO for Variable Selection in Cox Model With High-Dimensional Data: A Comprehensive Simulation Study

Sancar,

Onakpojeruo,

Inan

et al. 2023

IEEE Access

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In contemporary research, high-dimensional data has become more popular in many scientific fields with the rapid advancement of technology in collecting and storing large datasets. As in any modeling process with high-dimensional data, it is very important to accurately identify a subset of the features and reduce the dimensionality in the Cox modeling process in the case of high-dimensionality. Numerous penalized techniques for the Cox model with high-dimensional data have been developed to handle the multicollinearity problem and decrease variability. Adaptive Elastic-net is one of the penalized methods used for feature selection that both handles the grouping effect and has the oracle property. However, providing these advantageous properties of Adaptive Elastic-net for variable selection in the Cox model depends on the optimal selection of hyperparameters, 𝛼, and 𝜆 values. For this reason, the appropriate selection of these parameters is quite important. Hyperparameters are generally selected by maximizing k-fold cross-validated log partial likelihood based on grid search over (𝛼, 𝜆) for the model. However, this method does not guarantee optimal α and λ values. In grid search, hyperparameters are typically allowed to take values specified in a limited sequence in a grid. The purpose of this study is to propose a novel method to determine the optimum hyperparameters (α, λ) pair of Adaptive Elastic-net for variable selection in the Cox model with high dimensional data based on modified particle swarm optimization (MPSO). The introduced metaheuristicbased method has been evaluated by extensive simulation studies by comparing it with different traditional penalized methods using various evaluation criteria under different scenarios. According to the comprehensive simulation study, the proposed method outperforms other penalized methods in terms of both variable selection and prediction and estimation accuracy performance for the Cox model in investigating the high-dimensional data.INDEX TERMS Adaptive elastic net, cox model, high-dimensional data, modified particle swarm optimization, variable selection.

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COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Cited by 6 publications

References 35 publications

Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions

Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions

Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest

Adaptive Elastic Net Based on Modified PSO for Variable Selection in Cox Model With High-Dimensional Data: A Comprehensive Simulation Study

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