Development Of Predictive Models Research Articles

Development and validation of a risk prediction model for acute care use among patients with advanced cancer on clinical trials.

277 Background: Patients with advanced cancer on clinical trials are at high risk for unplanned Emergency Room visits (ER_visits) and hospital stays (HS). Identifying patients at highest risk could inform personalized intervention strategies. Methods: The data were from the SWOG Cancer Research Network. Medicare claims data were linked to data from advanced cancer clinical trials. The occurrence of any HS or ER visit within 1 year after enrollment was the primary endpoint. Patients must have had 6 months of continuous Medicare claims prior to registration to determine comorbid conditions prior to treatment, and 12 months of claims after registration (or until death) to detect outcomes. We examined 23 sociodemographic (age, sex, race, ethnicity, insurance status), geographic (rural or urban, area-level deprivation), clinical (disease severity, performance status), treatment (line of therapy, therapy type), and individual comorbidity factors to establish a predictive risk model. Model derivation was conducted in a 60% random sample and tested in the remaining 40%. Best subset selection with logistic regression was used with iterative application of 3-, 4-, and 5-fold cross validation repeated 10 times. Results: Among N=1397 total patients from 6 trials (lung, 3; prostate, 2; pancreas, 1), 33.9% were 75+ years, 20.3% were female, and 8.2% were Black. The overall proportion of patients with ≥1 HS/ER visit was 68.8%. In the training set (n=839), a best 5-variable risk was identified with adverse risk factors including cancer type (prostate vs other), performance status (PS; 0 vs. >=1), coronary artery disease (CAD; yes vs. no), hypertension (yes vs. no), and liver disease (yes vs. no). As cancer type was a study-level variable, we derived a risk model using PS, CAD, hypertension, and liver disease and adjusted for cancer type. There were no differences in adverse risk between those in with and without prostate cancer (interaction p-value=.44), justifying combining across cancer types. In the training set, patients with ≥2 risk factors (high risk; n=303, 36.1%) vs. 0/1 risk factor (low risk; n=536, 63.9%) had nearly a 3-fold increased odds of experiencing HS/ER visit (82.2% vs. 59.1%, OR=2.85, 95% CI, 2.01-4.03, p<.001). Results were successfully validated in the test set (OR=1.93, p=.002). In all patients, those in the highest risk quartile (3/4 factors) vs the lowest risk (0 factors) had nearly a fourfold increased odds of HS/ER visits (OR=3.92, 95% CI, 2.25-6.83, p<.001). Conclusions: Rates of HS_ER visits were high within the first year after enrolling on a trial for advanced cancer. We derived and validated a risk model that delineated strong differences in risk. With expansion of eligibility to include patients with more comorbid conditions, personalized interventions aimed at preventing acute care use could decrease the cost and improve the quality of cancer care.

Development of a Prognosis Prediction Model for Pediatric Sepsis Based on the NLPR

Development of a Prognosis Prediction Model for Pediatric Sepsis Based on the NLPR

Integrating Petrophysical, Hydrofracture, and Historical Production Data with Self-Attention-Based Deep Learning for Shale Oil Production Prediction

Summary As the energy industry increasingly turns to unconventional shale reservoirs to meet global demands, the development of advanced predictive models for shale oil production has become imperative. The inherent complexity of shale formations, coupled with the intricacies of hydraulic fracturing, poses significant challenges to efficient resource extraction. Our study leverages a substantial data set from the Ordos Basin to develop an advanced predictive model, integrating 18 parameters that blend static petrophysical attributes and dynamic factors, including hydraulic fracturing parameters and real-time pump pressure data. This holistic approach enables our self-attention (SA) model to accurately forecast future production rates by processing the complex interplay between reservoir characteristics and operational inputs. In testing across three wells, the model achieved average accuracies of 99.28% for daily oil production (DOP) and 99.25% for daily liquid production (DLP) over 20 days, surpassing traditional long short-term memory (LSTM) and gated recurrent unit (GRU) models, proving its efficacy in fractured well production forecasting. Furthermore, using the initial 30 days of production data as input, the model demonstrated its capability to predict DOP and DLP over a one-year period, achieving prediction accuracies of 96.2% for DOP and 99.6% for DLP rates. Our model’s profound implications for the shale industry include establishing a quantifiable link between key factors and production forecasts, guiding the optimization of controllable aspects, and serving as a decision-support tool for more efficient and cost-effective oil recovery.

Development of a multifactorial prediction model for commute mode choice in 10 983 Finnish public sector employees: a cross-sectional study

ObjectiveThe objective of this study is to examine the feasibility of using survey data to identify factors that predict commute mode choice.DesignThe study design is cross-sectional.SettingSurvey data from the Finnish...

Development of prediction models of COVID-19 vaccine uptake among Lebanese and Syrians in a district of Beirut, Lebanon: a population-based study

IntroductionVaccines are essential to prevent infection and reduce the morbidity of infectious diseases. Previous evidence has shown that migrants and refugees are particularly vulnerable to exclusion and discrimination, and low...

Development and validation of a model for early prediction of residual feed intake in beef cattle using plasma biomarkers

Identification of plasma biomarkers for feed efficiency in growing beef cattle offers a promising opportunity for developing prediction models to improve precision feeding strategies. However, these models must accurately predict feed efficiency at early stages of fattening. Our study aimed to evaluate the reliability of candidate biomarkers previously identified in late-fattening cattle when analyzed during early fattening stages and to develop diet-specific prediction equations for residual feed intake (RFI). From a total of 364 Charolais bulls across 7 cohorts, we selected 64 animals with extreme RFI values. The animals were fed either a corn‑ or grass-silage diets. These animals were chosen from 4 out of available 7 cohorts. Animals from three cohorts (24 high-RFI and 24 low-RFI, having a mean RFI difference of 1.48 kg/d) were used for biomarker confirmation and prediction model training. Animals from a fourth cohort (8 high-RFI and 8 low-RFI, having a mean RFI difference of 0.98 kg/d) were used for model external validation. Blood samples were collected at the beginning of the feed efficiency test (333±20 days), and plasma underwent targeted metabolomic for 630 metabolites, natural abundance of 15N (δ15N), insulin, and IGF-1 analysis. Seven previously identified plasma biomarkers for RFI in late-fattening beef cattle still kept their capability for discriminating low and high RFI animals when analyzed during early fattening stages (P < 0.05). Among these confirmed biomarkers, five were common for both grass- and corn-fed animals (creatinine, β-alanine, triglyceride TG18:0_34:2, symmetric dimethyl-arginine and phosphatidylcholine PC aa C30:2) while two were diet-specific (insulin-like growth factor 1 for grass silage-based diet, and isoleucine for corn silage-based diet. No new plasma biomarkers of RFI were identified at early-fattening stages (false discovery rate, FDR>0.05). Prediction models were developed based on seven confirmed RFI biomarkers analyzed during early-fattening. Two logistic regression models incorporating creatinine and either IGF-1 (for grass silage-based diet) or PC aa C30:2 (for corn silage-based diet) effectively distinguished between high and low-RFI animals with high sensitivity and specificity (area under the curve, AUC > 0.80). The biomarkers used in the models showed moderate to high repeatability between early and late fattening stages (45 < r <65). The models were successfully externally validated, with more than 85% of animals from the fourth cohort correctly classified. Once validated in larger cohorts and utilizing cost-effective and rapid analytical methods, these models could support precision feeding and breeding programs, aiming to reduce the cost of raising beef cattle.

Gene-diet interactions in carbonated sugar-sweetened beverage consumption and metabolic syndrome risk: A machine learning analysis in a large hospital-based cohort

Background and aimCarbonated sugar-sweetened beverages (CSSB) intake has been increasingly linked to metabolic diseases. To investigate the association between CSSB intake and metabolic syndrome (MetS) risk, and the interaction between genetic predisposition to CSSB intake and dietary patterns. MethodsWe examined a hospital-based cohort of 57,940 participants, categorized into low-CSSB (n=52,848) and high-CSSB (n=5,092) groups based on a 50 ml daily consumption cutoff. A genome-wide association study (GWAS) identified single-nucleotide polymorphisms (SNPs) associated with CSSB intake, and SNP-SNP/SNP-environment interactions were explored. Using XGBoost and deep neural network (DNN) approaches, we developed prediction models for CSSB intake. ResultsThe low- and high-CSSB groups daily consumed an average of 0.56 and 8.91 g sugar from the soda, respectively. The high-CSSB group had unhealthy dietary habits and a lower intake of carotenoids, folate, vitamins C and D, calcium, flavonoids, and phenols than the low-CSSB group, consistent with the results of the prediction models. A polygenic risk score (PRS) based on 6 selected SNPs, linked to genes involved in obesity, diabetes, and nervous system disorders, showed the strongest association with CSSB intake and insulin resistance. Notably, carbohydrate, fat, and Western-style diet (WSD) intake interacted with the PRS, with lower carbohydrate and higher fat and WSD intakes associated with a stronger PRS-sugar intake relationship. The prediction models by XGboost and DNN mainly included dietary factors to explain CSSB intake. ConclusionsA significant interplay between genetic predisposition and poor dietary habits, particularly increased CSSB intake associated with WSD, contributed to MetS risk. It suggested that personalized dietary interventions based on genetic profiles could mitigate MetS risk, especially in populations transitioning to Westernized diets.

Development of a CVD mortality risk score using nutritional predictors: A risk prediction model in the Golestan Cohort Study

Background and AimWe aimed to develop a dietary score using prediction model method for evaluating the risk of cardiovascular disease (CVD) mortality and suggesting a simple and practical scoring system within the healthcare context. Method and ResultsA total of 43878 adult participants (aged 37-80 years) from the Golestan Cohort Study (GCS) were included in analysis. A random split of the subjects into the derivation (n=28930) and the validation sets (n=14948) was done. The Cox proportional hazard model was used to develop prediction model for the 8-year risk of CVD mortality. The model’s discrimination and calibration were assessed by C-statistic and calibration plot, respectively. To enhance clinical utility, we devised a point-based scoring system derived from our model. This prediction model was developed by nine predictors including age, physical activity level (MET minutes/week), waist-to-hip ratio, tea intake (cup/day), vegetable intake (gr/1000 kcal/day), white meat intake (gr/1000 kcal/day), salt intake (gr/1000 kcal/day), dairy intake (Cup/1000 kcal/day), and percentage of protein intake. The model had an acceptable discrimination in both derivation (C-statistic: 0.76, p<0.001) and validation (C- statistic: 0.77, p<0.001) samples. Also, the calibration of model in both derivation and validation datasets was 0.81. ConclusionThis is the first attempt to develop a risk prediction model of CVD mortality and the risk scoring system by the majority of nutritional predictors in a large cohort study. This nutritional risk assessment tool is suitable for motivating at-risk individuals to make lifestyle and dietary pattern changes to reduce future risk to prevent health problems.

Current predictors for morbidity regarding choice of birth after a previous caesarean section, show poor predictive value in prediction modelling

IntroductionAfter a previous caesarean section, morbidity in the subsequent delivery in general is considered to depend on the probability of a vaginal birth after caesarean. However counselling could be improved by adding individualized probability of serious morbidity following either trial of labour or elective repeat caesarean section. The objective of this study was to develop prediction models for morbidity for both a repeat caesarean section and a trial of labor for a Dutch population. Material and methodsIn this cohort study, data were joined from three previous studies (SIMPLE 1, SIMPLE 2 and SIMPLE 2-implementation study). A cohort of 2592 women with one previous caesarean section and a singleton pregnancy who delivered ≥37 weeks, without a contraindication for vaginal delivery was formed. Maternal morbidity was defined as postpartum hemorrhage, blood transfusion, uterine rupture, ICU admittance or death. Neonatal morbidity was defined as asphyxia, NICU-admittance or death. Potential predictors for morbidity were chosen based on literature and expert opinion. Logistic regression was used to develop the models. Internal validation was intended using bootstrapping techniques. Main outcome measures were predictors for morbidity and for validation of the model we used the area under the receiver operating characteristic curve for discriminative capacity and calibration for accuracy. ResultsIn 324 out of the 2592 cases (12.7 %) maternal or fetal complications occurred. In general total morbidity was higher in women choosing TOL as compared to ERCS (p < 0.001). The performance of the several developed models was insufficient, the area under the receiver operating characteristic curve did not rise above 0.6. Due to poor model performance, before correction for overfitting, interval validation was not conducted. ConclusionIn this large cohort, developing a Dutch population based prediction model that aimed to improve counselling on the mode of delivery, by predicting individual chances of morbidity for different delivery modes was not possible, due to lack of performance. Further study could be directed to cut off on VBAC success rates to a more general advice regarding the safest mode of delivery.

Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) significantly affects pregnancy outcomes. Therefore, it is crucial to develop prediction models since they can guide timely interventions to reduce the incidence of GDM and its associated adverse effects. A total of 554 pregnant women were selected and their sociodemographic characteristics, clinical data and dietary data were collected. Dietary data was investigated by a validated semi-quantitative food frequency questionnaire (FFQ). We applied random forest mean decrease impurity for feature selection and the models are built using Logistic Regression, XGBoost, and LightGBM algorithms. The prediction performance of different models was compared by Accuracy, Sensitivity, Specificity, Area Under Curve (AUC) and Hosmer-Lemeshow test. Blood glucose, age, pre-pregnancy body mass index (BMI), triglycerides and high-density lipoprotein cholesterol (HDL) were the top five features according to the feature selection. Among the three algorithms, XGBoost performed best with an AUC of 0.788, LightGBM came second (AUC = 0.749), and Logistic Regression performed the worst (AUC = 0.712). In addition, XGBoost and LightGBM both achieved a fairly good performance when dietary information was included, surpassing their performance on the non-dietary dataset (0.788 vs. 0.718 in XGBoost; 0.749 vs. 0.726 in LightGBM). XGBoost and LightGBM algorithms outperform Logistic Regression in predicting GDM among the Chinese pregnant women. In addition, dietary data may have a positive effect on improving model performance, which deserves more in-depth investigation with larger sample size.

Optimizing the fairness of survival prediction models for racial/ethnic subgroups: A study on predicting post-operative survival in stage IA and IB non-small cell lung cancer.

380 Background: The recent surge of utilizing machine learning (ML) to develop prediction models for clinical decision-making aids is promising. However, these models can demonstrate racial bias due to inequities in real-world training data. In lung cancer, multiple models have been developed to predict prognosis, but none have been optimized to mitigate bias in performance among racial/ethnic subgroups. We developed a ML model to predict five-year survival in Stage 1A-1B non-small cell lung cancer (NSCLC), ensuring fairness on race. Methods: In the National Cancer Database, we identified patients with histopathologically confirmed stage 1A -1B NSCLC who underwent curative intent lobectomy from 2004 – 2017. We split the study cohort into a training and test sets (70%/30%). We trained and compared various ML models to predict 5-year overall survival. Patient demographic, clinical, and disease characteristics were used as input features for the models. To evaluate model fairness, we used the equalized odds ratio (eOR), which compares the true positive and false positive rates across groups; an eOR value of 1 represents equivalent rates across racial groups. We utilized 3 approaches to mitigate model bias and optimize for fairness of the best “naïve” model: grid search, threshold optimizer, and the exponentiated gradient methods. We evaluated model performance before and after bias mitigation using the area under the curve (AUC). Results: 124,298 patients fit our inclusion/exclusion criteria; 87% of patients were White, 8% were Black/African American, 3% Hispanic, and 2% Asian. Eighty percent of patients were diagnosed with stage 1A cancer; 20% had stage 1B cancer. The best naïve ML model, not optimized for fairness on race, had an eOR of 0.25 with an AUC of 0.66 (95% CI 0.65-0.66) overall. This model demonstrated an AUC of 0.65 (0.65-0.66) among white patients, 0.64 (0.62-0.66) among Black patients, 0.64 (0.60-0.68) among Asian patients, and 0.71 (0.68-0.74) among Hispanic patients. The threshold optimizer bias mitigation strategy improved fairness the most while maintaining similar overall performance of AUC 0.65 (0.64-0.66). With this strategy the eOR improved to 0.83 while AUC remained relatively stable across racial subgroups. Conclusions: We developed a ML model to predict 5-year survival in patients undergoing surgery for stage IA-IB NSCLC and employed model bias mitigation strategies that significantly improved model fairness, without diminishing overall performance. These strategies should be considered when developing prediction models for clinical decision making to avoid perpetuating disparities in care due to algorithm bias. Model performance metrics. Equalized Odds Ratio True Positive Rate False Positive Rate AUC Naive model 0.25 0.61 0.30 0.66 Threshold optimizer mitigated model 0.83 0.62 0.32 0.65

Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression.

Many models using the aid of artificial intelligence have been recently proposed to predict the progression of knee osteoarthritis. However, previous models have not been properly validated with an external data set or have reported poor predictive performances. Therefore, the purpose of this study was to design a machine learning model for knee osteoarthritis progression, focusing on high validation quality and clinical applicability. A retrospective analysis was conducted on prospectively collected data, using the Osteoarthritis Initiative data set (5966 knees) for model development and the Multicenter Osteoarthritis Study data set (3392 knees) for validation. The analysis aimed to predict Kellgren-Lawrence grade (KLG) progression over 4-5 years in knees with initial KLG of 0, 1, or 2. Possible predictors included demographics, comorbidities, history of meniscectomy, gait speed, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, and radiological findings. The Random Forest algorithm was employed for the predictive model development. Baseline KLG, contralateral knee osteoarthritis, lateral joint space narrowing (JSN) grade, BMI, medial JSN grade, and total WOMAC score were six features selected for the model in descending order of importance. Odds ratios of baseline KLG, contralateral knee osteoarthritis, and lateral JSN grade were 1.76, 2.59, and 4.74, respectively (all p < 0.001). The area-under-the-curve of the ROC curve in the validation set was 0.76 with an accuracy of 0.68 and an F1-score of 0.56. The progression of knee osteoarthritis in 4 ~ 5 years could be well-predicted using easily available variables. This simple and validated model may aid surgeons in knee osteoarthritis patient management.

Unfixed Seasonal Partition Based on Symbolic Aggregate Approximation for Forecasting Solar Power Generation Using Deep Learning

Solar energy is an important alternative energy source, and it is essential to forecast solar power generation for efficient power management. Due to the seasonal characteristics of weather features, seasonal data partition strategies help develop prediction models that perform better in extreme weather-related situations. Most existing studies rely on fixed season partitions, such as meteorological and astronomical, where the start and end dates are specific. However, even if the countries are in the same Northern or Southern Hemisphere, seasonal changes can occur due to abnormal climates such as global warming. Therefore, we propose a novel unfixed seasonal data partition based on Symbolic Aggregate Approximation (SAX) to forecast solar power generation. Here, symbolic representations generated by SAX are used to select seasonal features and obtain seasonal criteria. We then employ two-layer stacked LSTM and combine predictions from various seasonal features and partitions through ensemble methods. The datasets used in the experiments are from real-world solar panel plants such as in Gyeongju, South Korea; and in California, USA. The results of the experiments show that the proposed methods perform better than non-partitioned or fixed-partitioned solar power generation forecasts. They outperform them by 2.2% to 3.5%; and 1.6% to 6.5% in the Gyeongju and California datasets, respectively.

Prediction of intrapartum caesarean section in vaginal breech birth: development of models for nulliparous and multiparous women.

To develop prediction models for intrapartum caesarean section in vaginal breech birth. This single-center cohort-study included 262 nulliparous and 230 multiparous women attempting vaginalbreech birth. For both groups, we developed and (internally) validated three models for the prediction of intrapartum cesarean section. The prediction model for nulliparous women (AUC:0.67) included epidural analgesia (aOR 2.14; p=0.01), maternal height (aOR 0.64 per 10 cm; p=0.08), birthweight≥3.8 kg (aOR 2.45; p=0.03) and an interaction term describing the effect of OC if birthweight is≥3.8 kg (aOR0.24; p=0.04). An alternative model for nulliparous women which, instead of birthweight, included fetal abdominal circumference with a cut-off at 34 cm (aOR 1.93; p=0.04), showed similar performance (AUC: 0.68). The prediction model for multiparous women (AUC: 0.77) included prelabor rupture of membranes (aOR 0.31; p=0.03), epidural analgesia (aOR 2.42; p=0.07), maternal BMI (aOR 2.92 per 10 kg/m2; p=0.01) and maternal age (aOR 3.17 per decade; p=0.06). Our prediction models show the most relevant risk factors associated with intrapartum cesarean section in vaginal breech birth for both nulliparous and multiparous women. Importantly, this study clarifies the role of the OC byshowing that this parameter is only associated with intrapartum cesarean section if birthweight is above 3.8 kg (or abdominal circumference is above 34 cm). Conversely, knowing the OC when the birthweight is less than 3.8 kg (orabdominal circumference is less than 34 cm) did not improve prediction of this surgical outcome.

Molecular Interactions Governing the Rat Aryl Hydrocarbon Receptor Activities of Polycyclic Aromatic Compounds and Predictive Model Development.

Polycyclic aromatic compounds (PACs) exhibit rat aryl hydrocarbon receptor (rAhR) activities, leading to diverse biological or toxic effects. In this study, the key amino residues and molecular interactions that govern the rAhR activity of PACs were investigated using in silico strategies. The homology model of rAhR was first docked with 90 PACs to yield complexes, and the results of the molecular dynamics simulations of 16 typical complexes showed that the binding energies of the complexes range from -7.37 to -26.39 kcal/mol. The major contribution to the molecular interaction comes from van der Waals forces, and Pro295 and Arg316 become the key residues involved in most complexes. Two QSAR models were further developed to predict the rAhR activity of PACs (in terms of log IEQ for PACs without halogen substitutions and log%-TCDD-max for halogenated PACs). Both models have good predictive ability, robustness, and extrapolation ability. Molecular polarizability, electronegativity, size, and nucleophilicity are identified as the important factors affecting the rAhR activity of PACs. The developed models could be employed to predict the rAhR activity of other reactive PACs. This work provides insight into the mechanisms and interactions of the rAhR activity of PACs and assists in the assessment of their fate and risk in organisms.

Development of Machine Learning and Deep Learning Prediction Models for PM2.5 in Ho Chi Minh City, Vietnam

The application of machine learning and deep learning in air pollution management is becoming increasingly crucial, as these technologies enhance the accuracy of pollution prediction models, facilitating timely interventions and policy adjustments. They also facilitate the analysis of large datasets to identify pollution sources and trends, ultimately contributing to more effective and targeted environmental protection strategies. Ho Chi Minh City (HCMC), a major metropolitan area in southern Vietnam, has experienced a significant rise in air pollution levels, particularly PM2.5, in recent years, creating substantial risks to both public health and the environment. Given the challenges posed by air quality issues, it is essential to develop robust methodologies for predicting PM2.5 concentrations in HCMC. This study seeks to develop and evaluate multiple machine learning and deep learning models for predicting PM2.5 concentrations in HCMC, Vietnam, utilizing PM2.5 and meteorological data over 911 days, from 1 January 2021 to 30 June 2023. Six algorithms were applied: random forest (RF), extreme gradient boosting (XGB), support vector regression (SVR), artificial neural network (ANN), generalized regression neural network (GRNN), and convolutional neural network (CNN). The results indicated that the ANN is the most effective algorithm for predicting PM2.5 concentrations, with an index of agreement (IOA) value of 0.736 and the lowest prediction errors during the testing phase. These findings imply that the ANN algorithm could serve as an effective tool for predicting PM2.5 concentrations in urban environments, particularly in HCMC. This study provides valuable insights into the factors that affect PM2.5 concentrations in HCMC and emphasizes the capacity of AI methodologies in reducing atmospheric pollution. Additionally, it offers valuable insights for policymakers and health officials to implement targeted interventions aimed at reducing air pollution and improving public health.

Development of prediction model for risks of musculoskeletal chronic lumbopelvic pain in Indian women

Chronic lumbopelvic pain (CLPP) and its associated disabilities significantly affect women's social, professional, and personal lives. However, the specific factors contributing to CLPP in women remain unclear. To address this gap, this prospective cross-sectional study aims to identify the risk factors predicting CLPP in women and develop a prediction model that can predict CLPP in women. The study was conducted across Delhi, India, where free health camps were held, and 2400 women were assessed. Among the assessed individuals, the study revealed a high prevalence rate of CLPP among Indian women, standing at 70.4%. Seven risk factors namely, hamstring muscle tightness (> 20° on passive knee extension test), increased lumbar lordosis (> 11.5 cm of the lumbar lordotic index), reduced hip flexibility (> 15 cm on bent knee fallout test), altered foot posture (≥ 20 on foot posture index score), increased perception of psychological stress (> 25 on cohen’s perceived stress scale-10 score), reduced physical activity level (< 475 metabolic/minute on international physical activity questionnaire) and reduced performance of transversus abdominis muscle (≤ 5 on deep muscle contraction scale score) strongly predict the risks of CLPP in women. Identifying these risk factors is crucial for effectively preventing and managing CLPP symptoms, especially considering its high prevalence among Indian women. Health professionals should prioritize raising awareness about CLPP and its causative factors, as well as implementing strategies for early detection and intervention.

Advanced Predictive Modeling of Concrete Compressive Strength and Slump Characteristics: A Comparative Evaluation of BPNN, SVM, and RF Models Optimized via PSO

This study presents the development of predictive models for concrete performance, specifically targeting the compressive strength and slump value, utilizing the quantities of individual raw materials in the concrete mix design as input variables. Three distinct machine learning approaches—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were employed to establish the prediction models independently. In the model construction process, the Particle Swarm Optimization (PSO) algorithm was integrated with cross-validation to fine-tune the hyperparameters of each model, ensuring optimal performance. Following the completion of training and modeling, a comprehensive comparison of the predictive accuracy among the three models was conducted, with the aim of selecting the most suitable model for incorporation into an optimized objective function. The findings reveal that among the chosen machine learning techniques, BPNN exhibited superior predictive capabilities for the compressive strength of concrete. Specifically, in the validation set, BPNN achieved a high correlation coefficient (R) of 0.9531 between the predicted and actual outputs, accompanied by a low Root Mean Square Error (RMSE) of 4.2568 and a Mean Absolute Error (MAE) of 2.6627, indicating a precise and reliable prediction. Conversely, for the prediction of the concrete slump value, RF outperformed the other two models, demonstrating a correlation coefficient (R) of 0.8986, an RMSE of 9.4906, and an MAE of 5.5034 in the validation set. This underscores the effectiveness of RF in capturing the complexity and variability inherent in slump behavior. Overall, this research highlights the potential of integrating advanced machine learning algorithms with optimization techniques for enhancing the accuracy and efficiency of concrete performance predictions. The identified optimal models, BPNN for compressive strength and RF for slump, can serve as valuable tools for engineers and researchers in the field of construction materials, facilitating the design of concrete mixes tailored to specific performance requirements.

A study on source domain selection for transfer learning-based cross-building cooling load prediction

Transfer learning (TL) can utilize data from information-rich source domain buildings to help information-poor target domain buildings establish cooling load prediction models, thereby enabling cross-building cooling load prediction. However, the prediction performance varies based on the type of source domain building used, making the reasonable selection of source domain buildings crucial. Traditionally, data similarity of source-target building pairs is measured to identify the appropriate source domain building. Yet, this method will result in erroneous judgments if the similarity index used is inappropriate, the target domain building’s available data are scarce, or all candidate source domain buildings exhibit weak similarities. To address this issue, this study explores the selection of source domain buildings based on building feature consistency in a data-centric manner. Specifically, 81 buildings with different features and 240 source-target building pairs are designed. The TL method is then used to develop prediction models for each pair. The decision tree method is finally employed to evaluate the correlation between the feature consistency of source-target building pairs and model prediction performance. From this correlation, a selection strategy is derived that specifies the consistency requirements for three key features (scale, function, and climate zone) of source-target building pairs at different accuracy levels. This strategy offers a methodical guide for the selection of the source domain building.

Improve Metode Lightgbm untuk Prediksi Harga Mobil Bekas Menggunakan Hyper-Parameter Tuning

This study aims to predict used car prices using the LightGBM method and hyperparameter tuning techniques in the context of data science. The analysis process includes collecting historical data on used cars, preprocessing the data to clean and encode variables, and splitting the data into training and testing sets. The LightGBM model was trained and optimized through hyperparameter tuning using GridSearchCV to improve model performance. The model was evaluated using metrics such as Mean Squared Error (MSE) and R-squared. The results indicate that the well-optimized LightGBM model can accurately predict used car prices with high accuracy. The low MSE value (35207938112.028404) and high R-squared value (0.9462871489515565) demonstrate the model's excellent predictive quality. This research provides deeper insights into the factors influencing used car prices and contributes to the development of effective and reliable predictive models.