Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gyγ-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.

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Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2021/581022021 ◽

2021 ◽

Vol 10 (2) ◽

pp. 888-893

Keyword(s):

Prediction Model ◽

Model Performance ◽

Warning System ◽

Support Vector ◽

Ann Model ◽

Flood Prediction ◽

River Region ◽

Input Variables ◽

Using Data ◽

Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

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Application of WOA–SVR in Seed Vigor of High-Voltage Electric Field Treatment on Aged Cotton (Gossypium spp.) Seeds

Agronomy ◽

10.3390/agronomy12010088 ◽

2021 ◽

Vol 12 (1) ◽

pp. 88

Author(s):

Zhanhua Song ◽

Junxiang Ma ◽

Qian Peng ◽

Baoji Liu ◽

Fade Li ◽

...

Keyword(s):

Electric Field ◽

Prediction Model ◽

High Voltage ◽

Treatment Time ◽

Seed Vigor ◽

Germination Index ◽

Support Vector ◽

Gossypium Spp ◽

Whale Optimization ◽

Input Variables

When seeds are treated with a high-voltage electric field (HVEF) to improve seed vigor, due to the large differences in the biological electromagnetic effects on different types of seeds, the methods of variance analysis and regression analysis based on data statistics are generally used to construct the optimal electric field dose prediction model; however, the generalization performance of the prediction model tends to be poor. To solve this problem, the electric intensity, frequency and treatment time were taken as the input variables for hybrid support vector regression (SVR) analysis to establish the prediction model of the seed comprehensive germination index. The whale optimization algorithm (WOA) was used to optimize the kernel parameters of the SVR. The optimized hybrid WOA–SVR model predicted the optimal comprehensive germination index of aged cotton (Gossypium spp.) seeds to be 329, the optimal HVEF dosage was 3.64 kV/cm × 99 s, and the frequency was 1.4 Hz. The aged cotton seeds were treated with the optimal HVEF and the germination test was carried out. Compared with the check (CK), the comprehensive germination index of seeds increased by 48%. The research results provided a new method and new idea for the optimal design of parameters for seed treatment with HVEF.

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The Reconstruction and Extension of Terrestrial Water Storages Based On A Combined Prediction Model

10.21203/rs.3.rs-531840/v1 ◽

2021 ◽

Author(s):

Erhao Meng ◽

Shengzhi Huang ◽

Qiang Huang ◽

Linyin Cheng ◽

Wei Fang

Keyword(s):

Prediction Model ◽

Yunnan Province ◽

Water Storage ◽

Meteorological Drought ◽

Selection Strategy ◽

Support Vector ◽

Total Water ◽

Input Selection ◽

Soil Moisture Storage ◽

Input Variables

Abstract The monthly changes in total water storage (△TWS) can be employed for drought and flood monitoring and early warning and can be obtained from the total water storage anomalies (TWSA) of the Gravity Recovery and Climate Experiment (GRACE). However, the relatively short GRACE time series limits its further wide application. To this end, a combined prediction (CP) model including Support Vector Machine (SVM) and Artificial Neural Network (ANN) was proposed in this study for the reconstruction and extension of monthly TWSA from 1960 to 2012. Moreover, an innovative input selection strategy is proposed to build a monthly TWSA prediction model, in which the partial correlation algorithm is used to select the best input variables from candidate input variables. These candidate input variables include streamflow, precipitation, evaporation, and soil moisture storage (SMS). The Yunnan province, a typical humid area in China, was selected as a case study. The results show that: (1) The innovative input selection strategy effectively improves the simulation ability of the model, especially when the candidate input variables influence each other; (2) The performance of the CP model using the innovative input selection strategy is best; (3) The monthly △TWS obtained from the extension of TWSA recorded five of the seven extreme meteorological drought events in Yunnan Province from 1961 to 2001, therefore, the reliability of the expanded TWSA is better than GLDAS TWSA. Generally, the findings of this study showed that the CP model using an innovative input selection strategy is a useful and powerful tool for monthly TWSA prediction.

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Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients

Clinical and Applied Thrombosis/Hemostasis ◽

10.1177/10760296211040868 ◽

2021 ◽

Vol 27 ◽

pp. 107602962110408

Author(s):

Lengchen Hou ◽

Longjun Hu ◽

Wenxue Gao ◽

Wenbo Sheng ◽

Zedong Hao ◽

...

Keyword(s):

Growth Rate ◽

Pulmonary Embolism ◽

Prediction Model ◽

Risk Prediction ◽

Predictive Performance ◽

Hospitalized Patients ◽

Risk Prediction Model ◽

Gradient Boosting ◽

Support Vector ◽

D Dimer

The purpose of this study is to establish a novel pulmonary embolism (PE) risk prediction model based on machine learning (ML) methods and to evaluate the predictive performance of the model and the contribution of variables to the predictive performance. We conducted a retrospective study at the Shanghai Tenth People's Hospital and collected the clinical data of in-patients that received pulmonary computed tomography imaging between January 1, 2014 and December 31, 2018. We trained several ML models, including logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), compared the models with representative baseline algorithms, and investigated their predictability and feature interpretation. A total of 3619 patients were included in the study. We discovered that the GBDT model demonstrated the best prediction with an area under the curve value of 0.799, whereas those of the RF, LR, and SVM models were 0.791, 0.716, and 0.743, respectively. The sensibilities of the GBDT, LR, RF, and SVM models were 63.9%, 68.1%, 71.5%, and 75%, respectively; the specificities were 81.1%, 66.1, 72.7%, and 65.1%, respectively; and the accuracies were 77.8%, 66.5%, 72.5%, and 67%, respectively. We discovered that the maximum D-dimer level contributed the most to the outcome prediction, followed by the extreme growth rate of the plasma fibrinogen level, in-hospital duration, and extreme growth rate of the D-dimer level. The study demonstrates the superiority of the GBDT model in predicting the risk of PE in hospitalized patients. However, in order to be applied in clinical practice and provide support for clinical decision-making, the predictive performance of the model needs to be prospectively verified.

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Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

Water Practice & Technology ◽

10.2166/wpt.2006.017 ◽

2006 ◽

Vol 1 (1) ◽

Author(s):

K. Katayama ◽

K. Kimijima ◽

O. Yamanaka ◽

A. Nagaiwa ◽

Y. Ono

Keyword(s):

Principal Component Analysis ◽

Prediction Model ◽

Principal Components ◽

Prediction Method ◽

Principal Component ◽

Component Analysis ◽

Rainfall Data ◽

Radar Rainfall ◽

Input Variables ◽

Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

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Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

Mathematical Problems in Engineering ◽

10.1155/2020/8642430 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Shengpu Li ◽

Yize Sun

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Least Squares ◽

Transfer Rate ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Experimental Sample ◽

Machine Model ◽

Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽

10.3390/ma14133496 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3496

Author(s):

Haijun Wang ◽

Diqiu He ◽

Mingjian Liao ◽

Peng Liu ◽

Ruilin Lai

Keyword(s):

Aluminum Alloy ◽

Friction Stir Welding ◽

Prediction Model ◽

Real Time ◽

Support Vector ◽

Weld Quality ◽

Prediction Ability ◽

Friction Stir ◽

Temperature Signal ◽

2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

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Prediction of Incident Cancers in the Lifelines Population-Based Cohort

Cancers ◽

10.3390/cancers13092133 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2133

Author(s):

Francisco O. Cortés-Ibañez ◽

Sunil Belur Nagaraj ◽

Ludo Cornelissen ◽

Gerjan J. Navis ◽

Bert van der Vegt ◽

...

Keyword(s):

Cancer Incidence ◽

Binary Classification ◽

Predictive Performance ◽

Population Based ◽

Support Vector ◽

Clinical Variables ◽

Incident Cancer ◽

History Of ◽

Diagnosis Of Cancer ◽

Auc Value

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

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Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

Technology in Cancer Research & Treatment ◽

10.1177/15330338211024660 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110246

Author(s):

Jihwan Park ◽

Mi Jung Rho ◽

Hyong Woo Moon ◽

Jaewon Kim ◽

Chanjung Lee ◽

...

Keyword(s):

Prostate Cancer ◽

Random Forest ◽

Radical Prostatectomy ◽

Prediction Model ◽

Biochemical Recurrence ◽

Clinical Decision ◽

Outcome Variable ◽

Tertiary Hospitals ◽

Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

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An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽

10.3390/sym13020212 ◽

2021 ◽

Vol 13 (2) ◽

pp. 212

Author(s):

Yu-Wei Liu ◽

Huan Feng ◽

Heng-Yi Li ◽

Ling-Ling Li

Keyword(s):

Support Vector Machine ◽

Prediction Model ◽

Output Power ◽

Input Data ◽

Support Vector Machine Model ◽

Weather Conditions ◽

Clean Energy ◽

Support Vector ◽

Machine Model ◽

Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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