Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models

BackgroundFew studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.Methodology/Principal FindingsA total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC.Conclusions/SignificanceCompared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

Background. This study purposed to validate the use of artificial neural network (ANN) models for predicting myofascial pain control after dry needling and to compare the predictive capability of ANNs with that of support vector machine (SVM) and multiple linear regression (MLR). Methods. Totally 400 patients who have received dry needling treatments completed the Brief Pain Inventory (BPI) at baseline and at 1 year postoperatively. Results. Compared to the MLR and SVM models, the ANN model generally had smaller mean square error (MSE) and mean absolute percentage error (MAPE) values in the training dataset and testing dataset. Most ANN models had MAPE values ranging from 3.4% to 4.6% and most had high prediction accuracy. The global sensitivity analysis also showed that pretreatment BPI score was the best parameter for predicting pain after dry needling. Conclusion. Compared with the MLR and SVM models, the ANN model in this study was more accurate in predicting patient-reported BPI scores and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

This article presents an integrated algorithm consisting of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional linear regression (CLR) models for optimum long-term electricity price forecasting. Three ANN models and seven well-known FLR models along with a CLR model are applied all together to provide a robust framework for electricity price forecasting. Analysis of variance for a randomized complete block design and Fisher Least Significant Difference test are performed to compare forecasting results obtained by the ANN, FLR, and CLR models. Results indicate that there is a significant difference between the performance of ANN and FLR models in terms of mean absolute percentage error. Besides, it is shown that the CLR and FLR models considerably outperform the ANN models in this case. The proposed algorithm can be easily used in uncertain and complex environments due to its flexibility and is suitable for the long-term forecasting of electricity price.

Regression and ANN models for predicting MOR and MOE of heat-treated fir wood

PurposeThis research aims to develop conceptual phase building project cost forecasting models by exploring the relationship of existing plan shape complexity indices and general design morphology parameters with total construction cost.Design/methodology/approachPlan shape indices proposed to date by the literature for measuring building design complexity are critically reviewed. Building morphology is also dictated by town planning restrictions such as plot coverage ratio or number of storeys. This study analyses historical data collected from 49 residential building projects to develop multiple linear regression (MLR) and artificial neural network (ANN) models for forecasting construction cost. Existing plan shape coefficients are calculated to evaluate the geometrical complexity of sampled projects. Ten regression-based cost estimating equations are totally derived from stepwise backward and forward methods, and their predictive accuracy is contrasted: to performance levels reported in past studies and to ANN models developed in this research with multilayer perceptron architecture.FindingsAnalysis of plan shape indices revealed that 85.7% of examined past projects possess a high degree of design complexity, hence resulting in expensive initial decisions. This highlights the need for more effective early design stage decision-making by developing new building economic tools. The most accurate regression model, with a mean absolute percentage error (MAPE) of 19.2%, predicts the log of total cost from wall to floor index and total building envelope surface. Other explanatory variables resulting in MAPE values in the order of 20%–22% are total volume, volume above ground level, gross floor area below ground level, gross floor area per storey and total number of storeys. The overall MAPE of regression-based equations is 24.3% whilst ANN models are slightly more accurate with MAPE scores of 21.8% and 21.6% for one hidden and two hidden layers, respectively. The most accurate forecasting model in the research is the ANN with two hidden layers and the sigmoid activation function which predicts total building cost from total building volume (19.1%).Originality/valueThis paper introduces MLR-based and ANN-based conceptual construction cost forecasting models which are founded solely on building morphology design parameters and compare favourably with previous studies with an average predictive accuracy less than 25%. This paper is expected to be beneficial to both practitioners and academics in the built environment towards more effective cost planning of building projects. The methodology suggested can further be implemented in other countries provided that accurate and relevant data from historical projects are used.

Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

Computational intelligent techniques for predicting optical behavior of different materials

One of the most important qualitative aspects of wetland ecosystem management is preserving the natural quality of water in such environments. This would not be achievable unless continuous water quality monitoring is implemented. With the recent advances in remote sensing technology, this technology could assist us to produce accurate models for estimating water quality variables in the ecosystem of wetlands. The present study was carried out to evaluate the capability of remote sensing data to estimate the water quality variables [pH, total suspended solids (TSS), total dissolved solids (TDS), turbidity, nitrate, sulfate, phosphate, chloride and the concentration of chlorophyll a] in Zarivar International Wetland using linear regression (LR) and artificial neural network (ANN) models. For this purpose, spectral reflectance of bands 2, 3, 4 and 5 of the OLI sensor of Landsat 8 was utilized as the input data and the collected chemical and physical data of water samples were selected as the objective data for both ANN and LR models. Based on our results overall, ANN model was the proper model compared with LR model. The spectral reflectance in bands 5 and 4 of OLI sensor revealed the best results to estimate TDS, TSS, turbidity and chlorophyll in comparison with other used bands in ANN model, respectively. We conclude that OLI sensor data are an excellent means for studying physical properties of water quality and comparing its chemical properties.

<p>Uncertainty Analysis of Artificial Neural Network (ANN) And Support Vector Machine (SVM) Models in Predicting Monthly River Flow (Case Study: Ghezelozan River)</p>

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

Prediction of performance parameters in friction stir processing using ANN and multiple regression models

A single hidden layer Artificial Neural Network (ANN) model was developed to estimate a machinery energy ratio (MER) indicator, used to characterize and assess mechanization status of potato farms in Iran with a view point of energy expenditure in farm machinery. A wide range of variables of farming activities were examined. Initially, 90 attributes were used as input variables to predict desired MER output. Using regression analysis, 13 inputs were finally selected to model MER. Performance of developed ANN model was evaluated with various statistical measures including the coefficient of determination (R2), mean absolute percentage error (MAPE), mean squared error (MSE) and mean absolute error (MAE). The optimum ANN model had a 13 - 4 - 1 configuration. The values of the optimum model’s outputs correlated well, with R2 of 0.98. Value of MAPE calculated as 0.0001 for best ANN model, which indicate superiority of this model over other prediction models. Sensitivity analyses were also conducted to investigate the effects of each input item on the output value. Since the ANN model can predict this mechanization indicator for a target farming system in Hamadan province of Iran, it could be a good estimator for appraising mechanization of other regional farms. Also it overcomes some of the limitations of using simple data available from local databases as inputs that may contain errors. Key words: Potato, agricultural mechanization, machinery energy ratio, Artificial Neural Network.

Few studies of breast cancer surgery outcomes have used longitudinal data for more than five years. To validate the use of artificial neural network (ANN) models in predicting 5-year mortality for breast cancer surgery patients and to compare predictive accuracy between an ANN model and a multiple logistic regression (MLR) model. This study compared the performance of ANN and MLR models based on retrospective clinical data of 3,632 breast cancer surgery patients treated during 1996–2010. Global sensitivity score and analysis approach were also employed to assess the relative importance of variables and the relative significance of input parameters in the system model. In the training, testing, and validation groups of breast cancer surgery patients, the ANN model significantly outperformed the MLR model in terms of specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), accuracy, and area under the receiver operating characteristic curves. Surgeon volume was the most influential variable affecting 5-year mortality followed by hospital volume, age, and Charlson co-morbidity index (CCI) score. The ANN model achieved higher overall performance indices and was more accurate in predicting 5-year mortality, compared with the conventional MLR model.

Most reports compare artificial neural network (ANN) models and logistic regression models in only a single data set, and the essential issue of internal validity (reproducibility) of the models has not been adequately addressed. This study proposes to validate the use of the ANN model for predicting in-hospital mortality after traumatic brain injury (TBI) surgery and to compare the predictive accuracy of ANN with that of the logistic regression model. The authors of this study retrospectively analyzed 16,956 patients with TBI nationwide who were surgically treated in Taiwan between 1998 and 2009. For every 1000 pairs of ANN and logistic regression models, the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared using paired t-tests. A global sensitivity analysis was also performed to assess the relative importance of input parameters in the ANN model and to rank the variables in order of importance. The ANN model outperformed the logistic regression model in terms of accuracy in 95.15% of cases, in terms of Hosmer-Lemeshow statistics in 43.68% of cases, and in terms of the AUC in 89.14% of cases. The global sensitivity analysis of in-hospital mortality also showed that the most influential (sensitive) parameters in the ANN model were surgeon volume followed by hospital volume, Charlson comorbidity index score, length of stay, sex, and age. This work supports the continued use of ANNs for predictive modeling of neurosurgery outcomes. However, further studies are needed to confirm the clinical efficacy of the proposed model.

Mechanical properties (tensile strength (TS), modulus of elasticity in tensile (MET), flexural strength (FS), modulus of elasticity (MOE)) of the material to be obtained depending on the production parameters in the production of high-density polyethylene (HDPE) wood-polymer composites with Scots pine wood flour additive were predicted using Artificial Neural Networks (ANN) model and without destructive testing. In the first stage of the study, an ANN model was developed using data from 56 different studies in the literature on the mechanical properties of wood polymer composites. In the second stage, in order to determine the reliability of the model, output values were estimated using input parameters that had not been used in training and testing of the model. Based on the same input parameters, test specimens were produced and mechanical tests were performed. The results obtained from the experiments and ANN model were compared by considering the mean absolute percentage error (MAPE) value. The coefficient of determination (R2) values obtained in the training and testing phase of the ANN models were all higher than 0.90. In this way, the mechanical properties of the wood polymer composite were successfully predicted by the ANN model. Because most of the MAPE values obtained from the mechanical tests were below 10%, the model was considered a reliable model.