Precision Unveiled in Unborn: A Cutting-Edge Hybrid Machine Learning Approach for Fetal Health State Classification.

Minimum Ignition Energy (MIE) prediction models for ignition sensitive fuels using machine learning methods

Reviewed by: Death Before Birth: Fetal Health and Mortality in Historical Perspective Karol K. Weaver Robert Woods. Death Before Birth: Fetal Health and Mortality in Historical Perspective. Oxford: Oxford University Press, 2009. xvii + 294 pp. Ill. $110.00 (978-0-19-954275-8). Robert Woods’s Death Before Birth: Fetal Health and Mortality in Historical Perspective is an intriguing book. Its subject matter concerns fetal health and mortality with a focus on how fetal deaths declined over time. Woods clearly explains the difficulties the topic presents to researchers. Despite the issues faced, Woods deftly applies both demography and history to the subject. The resulting work is a text that will inspire others to take up the history of the fetus, fetal health, and fetal mortality. Woods notes that historians have looked at multiple actors when studying the history of pregnancy and birth. He points out that researchers have focused their investigations on pregnant women, midwives, professional medical practitioners, and infants. His book, however, concentrates on one of the most important, but overlooked, actors during pregnancy and birth—the fetus. Specifically, Woods explores the history of fetal health and mortality. He concludes that the decrease in fetal mortality was aided by the availability of antibiotics in the twentieth century, disease prevention as a result of vaccination programs, and improvements in obstetrical care. The author shows that the topic of fetal health and mortality is difficult to study. Woods explains that he faced several issues when approaching the subject matter: the language used to describe fetal health and mortality is varied, there are numerous definitions of various fetal incidents, the recording of age depends on the culture in which it takes place, the registration of fetal deaths differs depending on the purpose of the record and on who is doing the recording, and the causes of fetal death are often difficult to determine. Woods, for example, presents the varied definitions related to fetal mortality via an excellent table; table 2.1 (pp. 15–16) succinctly lays out the multiple words that can be employed to describe fetal health and mortality. Through the use of history and demography, Woods traces developments in fetal health and mortality. His historical analysis depends on several types of primary sources: parish registers, the case notes of midwives and man-midwives, and the clinical reports of obstetricians. Woods not only analyzes these sources but also includes extended passages from several primary sources for the reader. His inclusion of a selection of observations from Sarah Stone’s A Complete Practice of Midwifery allows the reader into the historical process and shows that, despite being a “neglected area” (p. 1) of study, the history of fetal health and mortality is possible. In addition to history, Woods, a professor of geography, employs demography. He complements his written demographic analysis with graphs and [End Page 653] charts. Woods summarizes his methodological approach when he states, “This study is avowedly anti-disciplinary; it does not offer a history in the normal sense, rather it wants to know how and why change occurred in the long term and it will be prepared to use whatever is available and relevant to reach that goal” (p. 9). Death Before Birth will appeal to a variety of readers. Historians will find a work that focuses on a topic that has been neglected by scholars who specialize in the history of birth and pregnancy. The volume demonstrates that the sources are there to complete further work on the medical history of the fetus and alerts researchers to some of the difficulties they might face. Students of geography also will appreciate Woods’s demographic analysis. Woods has opened a new scholarly avenue and has provided future researchers with the map to investigate further the field of fetal health and mortality. Karol K. Weaver Susquehanna University Copyright © 2011 The Johns Hopkins University Press

BackgroundGastrointestinal bleeding (GIB) is a common complication following Type A aortic dissection (TAAD) surgery, significantly impacting prognosis and increasing mortality risk. This study developed and validated a predictive model based on machine learning (ML) algorithms to enable early and precise assessment of postoperative GIB risk in TAAD patients.MethodsMedical records of patients who underwent TAAD surgery at Shanxi Bethune Hospital from January 2019 to September 2024 were retrospectively collected. Predictors were screened using LASSO regression, and four ML algorithms—Random Forest (RF), K-nearest neighbor (KNN), Support Vector Machines (SVM), and Decision Tree (DT)—were employed to construct models for predicting postoperative GIB risk. The dataset was divided into training and validation sets in a 7:3 ratio. Predictive performance was evaluated and compared using Receiver Operating Characteristic (ROC) curves and DeLong tests. Calibration curves and decision curve analysis (DCA) were used to assess model calibration and clinical utility. The SHapley Additive exPlanation (SHAP) algorithm was applied for interpretability analysis. This study adhered to the “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD+AI) guidelines.”ResultsA total of 525 TAAD patients were included, with 63 (12%) developing GIB. Nine predictors were selected via LASSO regression for model construction. The RF model outperformed the SVM, KNN, and DT models in predicting postoperative GIB, with areas under the ROC curve (AUC) of 0.933, 0.892, 0.902, and 0.768, respectively, showing statistically significant differences (DeLong test, P < 0.05). Calibration curves and DCA further confirmed the RF model’s excellent calibration and clinical utility. SHAP analysis identified the three most influential clinical features on the RF model’s output: duration of mechanical ventilation (MV), Time to aortic occlusion, and red blood cell (RBC) transfusion.ConclusionThe machine learning-based predictive model effectively assesses postoperative GIB risk in TAAD patients, aiding healthcare providers in early identification of risk factors and implementation of targeted preventive strategies.

ObjectiveTracking global health funding is a crucial but time consuming and labor-intensive process. This study aimed to develop a framework to automate the tracking of global health spending using natural language processing (NLP) and machine learning (ML) algorithms. We used the global common goods for health (CGH) categories developed by Schäferhoff et al. to design and evaluate ML models.MethodsWe used data curated by Schäferhoff et al., which tracked the official development assistance (ODA) disbursements to global CGH for 2013, 2015, and 2017, for training and validating the ML models. To process raw text, we implemented different NLP techniques, such as removing stop words, lemmatization, and creation of synthetic text, to balance the dataset. We used four supervised learning ML algorithms—random forest (RF), XGBOOST, support vector machine (SVM), and multinomial naïve Bayes (MNB) (see Glossary)—to train and test the pre-coded dataset, and applied the best model on dataset that hasn't been manually coded to predict the financing for CGH in 2019.ResultsAfter we trained the machine on the training dataset (n = 10,534), the weighted average F1-scores (a measure of a ML model's performance) on the testing dataset (n = 2,634) ranked 0.79–0.83 among four models, and the RF model had the best performance (F1-score = 0.83). The predicted total donor support for CGH projects by the RF model was $2.24 billion across 3 years, which was very close to the finding of $2.25 billion derived from coding and classification by humans. By applying the trained RF model on the 2019 dataset, we predicted that the total funding for global CGH was about $2.7 billion for 730 CGH projects.ConclusionWe have demonstrated that NLP and ML can be a feasible and efficient way to classify health projects into different global CGH categories, and thus track health funding for CGH routinely using data from publicly available databases.

On the use of machine learning methods for mPSD calibration in HDR brachytherapy.

Monitoring crop development and mapping cultivated areas are important for reducing risks to food security due to climate change. Remote sensing techniques contribute significantly to the efficient and effective management of agricultural production. In this study, agricultural fields (sunflower, wheat, maize, oat, chickpea, sugar beet, alfalfa, onion, fallow) and other fields (non-agricultural, pasture, lake) were identified by using Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms with Sentinel-2 and Landsat-8 images in the area covering Polatlı, Haymana and Gölbaşı districts of Ankara province Multi-temporal images were used to distinguish winter and summer crops, taking into account crop development periods. As a result of classification; the overall accuracy of RF and SVM models with S2 images are 89.5% and 84.6% and kappa coefficients are 0.88 and 0.83, while the overall accuracy of RF and SVM models with L8 images are 79% and 78.1% and kappa coefficients are 0.76 and 0.75. RF model was found to have higher prediction accuracy than SVM. Sentinel-2 imagery has a higher accuracy in all classes compared to Landsat-8, indicating that Sentinel-2 imagery with its high temporal and spatial resolution is more suitable and has a great potential for agricultural crop pattern detection.

At present, in order to minimize the risk of automobile sales and maximize the sales profit, various machine learning algorithms are widely used to predict the market acceptance of a car. This paper proposes to use the stochastic forest algorithm, an integrated learning algorithm, to model the characteristics of vehicles, predict the market acceptance of vehicles, and use the Gini importance index to evaluate the characteristics that have the greatest impact on the market acceptance of vehicles. The research of this paper can be divided into the following points: The random forest algorithm is used to model the characteristics of the automobile, and the probability distribution of the automobile market acceptance is obtained, so as to predict the automobile market acceptance. The regression model is established, and the Gini importance index, an important parameter in the random forest model, is used as the standard to evaluate the characteristics that have the greatest impact on the market acceptance of a car. According to the experimental results, the prediction accuracy of random forest prediction model is 98%, which is higher than other machine learning algorithms such as support vector machine. In addition, through the quantitative analysis of Gini importance index of different vehicle characteristics, we can finally conclude that safety has the greatest impact on vehicle market acceptance.

Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning. However, no work has been reported to automate LULC performance modeling for their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes and automate the LULC performance modeling with six ML algorithms-Random Forest, Support Vector Machine with Linear kernel, Support Vector Machine with Radial basis function kernel, Artificial Neural Network, Naïve Bayes, and Generalised Linear Model for the city of Melbourne, Australia on Sentinel-2A images. Experimental results show that the Random Forest outperforms remaining ML algorithms in the classification accuracy (0.99) on all schemes. The robustness and statistical analysis of the ML algorithms (for example, Random Forest imparts over 0.99 F1-score for all five categories and p value le 0.05 from Wilcoxon ranked test over accuracy measures) against varying training splits demonstrate the effectiveness of the proposed schemes. Thus, providing a robust measure of LULC maps in city planning.

Urban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

Reservoir fluid PVT properties are measured in the laboratory for various use in reservoir engineering evaluation and estimation. Despite the indispensability of these PVT parameters, PVT lab data are seldomly available and if available may be unreliable. Instead, various empirical models have been developed and used in the industry. These empirical models are inherently inaccurate when used to predict PVT properties of fluid from different geological region with different depositional environment and fingerprint. Artificial Intelligence (AI) has evolved over the years and provided some algorithms with potentials to develop accurate predictive model for the prediction of bubblepoint pressure. This work tested some AI algorithms, compared performances and choose random forest regression algorithm in developing a robust predictive model for the estimation of bubblepoint pressure. Two thousand five hundred and twenty-two datasets obtained from oil reservoirs in different geographical locations were used for the feature scaling of input data, training and testing of the models. The independent variables, gas-oil ratio, temperature, oil density and gas density were confirmed to have key influence on the dependent variable Bubblepoint pressure The random forest model developed uses ensemble learning approach, combines predictions from multiple machine learning algorithms by averaging all predictions to make a more accurate prediction. The ‘forest’ generated by the random forest algorithm was trained through bootstrap aggregating. This is an ensemble meta-algorithm that improves the accuracy of machine learning algorithms. Percentage data split was 70% training and 30% testing. The reliability, accuracy and completeness of the predictive model capability were computed through performance indices such as the root mean square error (RMSE) and mean absolute error (MAE). The best network architecture was determined along with the corresponding test set RMSE, and Correlation coefficient. Statistical and graphical error analysis of the results showed that the random forest model performed better than existing models with 0.98 correlation coefficients for bubblepoint pressure. Better accuracy of reservoir properties prediction could be achieved using this random forest reservoir fluid properties prediction model.

Modeling soil loss under rainfall events using machine learning algorithms

To establish predictive models based on random forest and XGBoost machine learning algorithm and to investigate their value in predicting early stone-free rate (SFR) after flexible ureteroscopic lithotripsy (fURL) in patients with renal stones. The clinical data of 201 patients with renal stones who underwent fURL were retrospectively investigated. According to the stone-free standard, the patients were divided into stone-free group (SF group) and stone-residual group (SR group). We compared a number of factors including patient age, body mass index (BMI), stone number, stone volume, stone density and hydronephrosis between the two groups. For low calyceal calculi, renal anatomic parameters including infundibular angle (IPA), infundibular width (IW), infundibular length (IL) and pelvic calyceal height (PCH), would be measured. We brought above potential predictive factors into random forest and XGBoost machine learning algorithm respectively to develop two predictive models. The receiver operating characteristic curve (ROC curve) was established in order to test the predictive ability of the model. Clinical data of 71 patients were collected prospectively to validate the predictive models externally. In this study, 201 fURL operations were successfully completed. The one-phase early SFR was 61.2%. We built two predictive models based on random forest and XGBoost machine learning algorithm. The predictive variables' importance scores were obtained. The area under the ROC curve (AUROC) of the two predictive models for early stone clearance status prediction was 0.77. In the study, 71 test samples were used for external validation. The results showed that the total predictive accuracy, predictive specificity and predictive sensitivity of the random forest and XGBoost models were 75.7%, 82.6%, 60.0%, and 81.4%, 87.0%, 68.0%, respectively. The first four predictive variables in importance were stone volume, mean stone density, maximal stone density and BMI in both random forest and XGBoost predictive models. The predictive models based on random forest and XGBoost machine learning algorithm can predict postoperative early stone status after fURL for renal stones accurately, which will facilitate preoperative evaluation and clinical decision-making. Stone volume, mean stone density, maximal stone density and BMI may be the important predictive factors affecting early SFR after fURL for renal stones.

Soil salinity monitoring requires accurate measurement of saturated paste extract electrical conductivity (ECe) which is considered the most trustworthy measure of salinity hazard in many laboratories globally. It’s a time-consuming and technically demanding process. In contrast, the measurement of EC values from the 1:1, 1:2.5, and 1:5 soil-to-water ratios is simple, rapid and cheap. This study aims to develop a pedotransfer functions to estimate ECe from diluted soil-to-water extracts (EC1:1, EC1:2.5, EC1:5) and presents an innovative FTIR spectroscopy approach coupled with machine learning for rapid EC prediction. A total of 59 soil samples were collected from 22 profiles across three depths (0–20, 20-50 and 50-100 cm) in Morocco and analyzed for EC at different soil-to-water extracts and scanned using a Bruker-Tensor-II-HTS-XT spectrometer. Random Forest (RF) and Partial Least Squares Regression (PLSR) models were employed to predict ECe and diluted EC values. Results demonstrated a significant linear correlation between the ECe and diluted extract EC values, with an R2&gt;0.89 across all extracts. In addition, the conversion factors (CF) (ECe = CF × EC soil-to-water ratios) varied significantly among soil types, indicating the critical role of soil-type parameters for accurate ECe estimation. FTIR developed models demonstrated high predictive accuracy across all soil-to-water extracts (R2 = 0.86-0.91, RMSE = 0.41-3.69 dS/m), with distinct spectral features at 1970-2550 cm⁻1 and 2867-3086 cm⁻1 identified as the most sensitive regions for EC prediction. Random Forest (RF) models accurately predicted ECₑ from EC1:5 (R2 = 0.92, RMSE = 2.05 dS/m), with enhanced performance when including CEC and CaCO₃ content (R2=0.95, RMSE=1.51 dS/m). In conclusion, FTIR spectroscopy combined with machine learning offers an accurate, rapid and minimal sample preparation method to make it particularly valuable for large-scale precision agriculture. Our findings demonstrate that mid-infrared spectroscopy enabling a rapid ECₑ estimation without saturated paste analysis a significant advancement for salinity hazard monitoring. Graphical Abstract This study aims to develop pedotransfer functions to convert soil electrical conductivity of the saturated paste extract (ECe), a key indicator of soil salinity based on easily measured soil-to-water extract ratios (1:1, 1:2.5, and 1:5) in the irrigated perimeter of the Triffa plain, northeastern of Morocco. In addition, it presents an innovative approach for rapid estimation of the ECe using mid-infrared spectroscopy combined with machine learning techniques. Soil sampling was conducted across three depth intervals and different soil types including Mollisols, Ultisols, Histosols, Entisols and Aridisols. Laboratory analyses included EC measurements from both saturated paste extracts and diluted soil-to-water extracts was conducted. Simultaneously, mid-infrared spectral data were scanned using Fourier-transform infrared (FTIR) spectroscopy. Machine learning algorithms, specifically Random Forest (RF) and Partial Least Squares Regression (PLSR) were used to establish relationships between spectral data and EC values. The study also evaluates the influence of integrating two key soil properties, calcium carbonate (CaCO₃) content and cation exchange capacity (CEC), on model performance and prediction accuracy. The results reveal a strong correlation between the ECe and soil-to-water ratio of 1:1, 1:2.5 and 1:5 with an R2&gt;0,89 across all ratios. In addition, the conversion factors (CF) used to transform soil-to-water ratios into ECe (ECe = CF × EC soil-to-water) varied significantly by soil type for 1:1, 1:2.5 and 1: respectively: Mollisols (1.87, 5.30, 9.52), Ultisols (1.77, 6.03, 8.66), Histosols (2.32, 6.73, 8.74), Aridisols (1.82, 3.76, 7.57), and combined soils (2.09, 5.51, 8.60). Furthermore, the models developed from Mid-infrared spectral data were validated and were recorded as high accuracy for EC1:1 (R2=0.86, RMSE=1.65 dS/m), EC1:2.5 (R2=0.91, RMSE=0.41 dS/m), EC1:5 (R2=0.86, RMSE=0.42 dS/m), and ECe (R2=0.87, RMSE=3.69 dS/m). Moreover, the study reveals that spectral ranges 1970–2550 cm⁻1 and 2867–3086 cm⁻1 were identified as the most sensitive for EC prediction. On the other hand, RF models also demonstrated the strong model performance for ECe prediction from EC1:5 ratio (R2=0.92 and RMSE=2.05 dS/m). Worth noting that integration of soil properties such as CEC and CaCO3, has enhanced the model prediction performances (i.e. R2=0.95 and RMSE=1.51 dS/m). In concluding, FTIR spectroscopy enables accurate prediction of ECe using the (EC) of diluted soil-to-water extracts of 1:1, 1:2.5 and 1:5. Additionally, the RF machine learning algorithms, demonstrated strong potential in estimating ECe from EC1:5, with CEC and CaCO₃ serving a key role in enhancing model performance. Finally, the study recommends that mid FTIR spectroscopy coupled with chemometrics method is a robust, quick and cost-effective method for ECe measurement for soil salinity monitoring.

BackgroundNew-onset atrial fibrillation (NOAF) increases the risk of embolism and sudden death in critically ill patients; however, limited data exist attempting to identify modifiable risk factors and predict the incidence of NOAF. We aimed to investigate the risk factors for NOAF and develop an optimized clinical prediction model based on machine learning algorithms.Materials and methodsData from patients admitted to the intensive care unit (ICU) of the Affiliated Hospital of Nanjing University of Chinese Medicine from August 2019 to January 2022 were retrospectively analyzed. LASSO regression and Random Forest (RF) algorithms were used to screen predictive variables. Logistic Regression, RF, Gradient Boosting and Support Vector Machine models were constructed to evaluate the recognition ability of different machine learning algorithms. The confusion matrix and calibration curve were used to assess the degree of accuracy of the four models. Decision curve analysis (DCA) was conducted to evaluate the utility of the model in decision-making. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were also calculated to evaluate the performance of the models. The learning curves of the four models were plotted to evaluate the precision of different models. The SHapley Additive exPlanations (SHAP) was used to explain the supreme-performing model.ResultsIn total, 417 patients were enrolled in the study, and 333 patients were allocated to the training group and 84 to the validation group. The baseline characteristic distributions were similar between the two groups. Age, heart rate, mean arterial pressure, activated partial thromboplastin time, and brain natriuretic peptide were revealed as independent predictors of NOAF by LASSO regression and the RF algorithm. The RF model had the best performance, with the area under the receiver operator characteristic curve (AUROC) of 0.758, the area under the precision-recall curve (AUPRC) of 0.524, and accuracy of 0.735 in the training set, paralleled by AUROC of 0.796, AUPRC of 0.686, and accuracy of 0.702 in the validation set. The confusion matrix and calibration curves showed that RF had the best performance. DCAs also showed that the RF model provided the highest net benefit in the clinical setting. The NRI results showed that the RF significantly improved reclassification ability compared to the baseline model (NRI = 0.38). The IDI results further demonstrated a moderate improvement in discrimination ability for the RF (IDI = 0.033) compared to the baseline. The learning curves revealed that RF also showed superior performance. SHAP could be used visualized individual NOAF risk predicted by the model.ConclusionsThe RF model exhibited the best performance in predicting NOAF in critically ill patients and has the potential to help clinicians identify high-risk patients and guide clinical decision making.

Background: With the rapidly increasing population of elderly people, dental extraction in elderly individuals with cardiovascular diseases (CVDs) has become quite common. The issue of how to assure the safety of elderly patients with CVDs undergoing dental extraction has perplexed dentists and internists for many years. And it is important to derive an appropriate risk prediction tool for this population. Objectives: The aim of this retrospective, observational study was to establish and validate a prediction model based on the random forest (RF) algorithm for the risk of cardiac complications of dental extraction in elderly patients with CVDs. Methods: Between August 2017 and May 2018, a total of 603 patients who fulfilled the inclusion criteria were used to create a training set. An independent test set contained 230 patients between June 2018 and July 2018. Data regarding clinical parameters, laboratory tests, clinical examinations before dental extraction, and 1-week follow-up were retrieved. Predictors were identified by using logistic regression (LR) with penalized LASSO (least absolute shrinkage and selection operator) variable selection. Then, a prediction model was constructed based on the RF algorithm by using a 5-fold cross-validation method. Results: The training set, based on 603 participants, including 282 men and 321 women, had an average participant age of 72.38 ± 8.31 years. Using feature selection methods, 11 predictors for risk of cardiac complications were screened out. When the RF model was constructed, its overall classification accuracy was 0.82 at the optimal cutoff value of 18.5%. In comparison to the LR model, the RF model showed a superior predictive performance. The AUROC (area under the receiver operating characteristic curve) scores of the RF and LR models were 0.83 and 0.80, respectively, in the independent test set. The AUPRC (area under the precision-recall curve) scores of the RF and LR models were 0.56 and 0.35, respectively, in the independent test set. Conclusion: The RF-based prediction model is expected to be applicable for preoperative clinical assessment for preventing cardiac complications in elderly patients with CVDs undergoing dental extraction. The findings may aid physicians and dentists in making more informed recommendations to prevent cardiac complications in this patient population.