Potential Of Machine Learning Models Research Articles

Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments.

In this study, we propose a novel framework that utilizes deep learning and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of seven years. This study included subjects (1832 subjects, 3276 knees) from the baseline of the Multicenter Osteoarthritis Study (MOST). Patellofemoral joint regions-of interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. An end-to-end deep learning method was developed for predicting PFOA progression based on imaging data in a 5-fold cross-validation setting. To evaluate the performance of the models, a set of baselines based on known risk factors were developed and analyzed using gradient boosting machine (GBM). Risk factors included age, sex, BMI and WOMAC score, and the radiographic osteoarthritis stage of the tibiofemoral joint (KL score). Finally, to increase predictive power, we trained an ensemble model using both imaging and clinical data. Among the individual models, the performance of our deep convolutional neural network attention model achieved the best performance with an AUC of 0.856 and AP of 0.431; slightly outperforming the deep learning approach without attention (AUC=0.832, AP= 0.4) and the best performing reference GBM model (AUC=0.767, AP= 0.334). The inclusion of imaging data and clinical variables in an ensemble model allowed statistically more powerful prediction of PFOA progression (AUC = 0.865, AP=0.447), although the clinical significance of this minor performance gain remains unknown. The spatial attention module improved the predictive performance of the backbone model, and the visual interpretation of attention maps focused on the joint space and the regions where osteophytes typically occur. This study demonstrated the potential of machine learning models to predict the progression of PFOA using imaging and clinical variables. These models could be used to identify patients who are at high risk of progression and prioritize them for new treatments. However, even though the accuracy of the models were excellent in this study using the MOST dataset, they should be still validated using external patient cohorts in the future.

Data science integrated with computational fluid dynamics for particle collision modeling in fluidized bed

Fluidized beds are critical in numerous industrial applications, including chemical processing and energy production. Accurate modeling of solid particles and fluids within these beds is essential for process optimization and improved efficiency. In this study, we developed a machine learning model to predict the coefficient of restitution (COR) of solid particles in fluidized beds. Four variables—collision velocity, effective temperature, effective mass, and effective elastic modulus—were used in the model. Our dataset comprised 2446 data points from previous literature. We employed self-organizing map (SOM) and artificial neural network (ANN) approaches for data analysis. The initial ANN model, which did not incorporate data clustering, exhibited an impressive coefficient of determination (R-squared) of 0.989, indicating its high accuracy. To enhance the model further, we clustered the data into four groups using SOM and developed separate ANN models for each group. All four models achieved R-squared greater than 0.99, illustrating the effectiveness of data clustering. The resulting model can be integrated into simulation software, such as MFIX, to provide a more precise representation of fluidized bed behavior across various industrial settings. The findings emphasize the potential of machine learning models to enhance fluidized bed simulations, leading to increased efficiency and cost-effectiveness in industrial processes. Future studies should explore the inclusion of additional variables and extend the model's application to different industrial processes. Additionally, incorporating recommendations for optimizing fluidized bed behavior based on the model's predictions would provide valuable insights for process engineers.

Suspended sediment load modeling using Hydro-Climate variables and Machine learning

Machine learning (ML) models have the potential to improve the accuracy of Suspended Sediment Load (SSL) predictions. However, their ability to incorporate climate data to enhance SSL predictions remains underexplored. This study investigates the efficacy of ML models in modeling sediment transport using hydro-climate variables as input data. Boosted decision tree models, including Xgboost, LightGBM, and Catboost, were employed to estimate SSL using hydro-climate variables such as streamflow and precipitation, as well as maximum and minimum temperatures. The models were trained, optimized, and evaluated using either climate/streamflow data or a combination of both, encompassing 47 gauges across the contiguous United States (CONUS) between 1950 and 2022. The results demonstrate that streamflow data alone is insufficient for accurate SSL estimation in all rivers. Consequently, integrating climate data and streamflow data proved effective in predicting SSL at the daily scale across diverse climate zones with commendable accuracy. The models achieved an average Nash-Sutcliffe Efficiency (NSE) of > 0.66, Kling-Gupta Efficiency (KGE) of > 0.67, and Willmott Index (WI) of > 0.83 during the unseen (test) phase. These findings highlight the potential of ML models, particularly when combining climate data with streamflow, to enhance SSL predictions and improve our understanding of sediment transport dynamics.

The potential of machine learning models to identify malnutrition diagnosed by GLIM combined with NRS-2002 in colorectal cancer patients without weight loss information

Background & AimsThe key step of the Global Leadership Initiative on Malnutrition (GLIM) is nutritional risk screening, while the most appropriate screening tool for colorectal cancer (CRC) patients is yet unknown. The GLIM diagnosis relies on weight loss information, and bias or even failure to recall patients’ historical weight can cause misestimates of malnutrition. We aimed to compare the suitability of several screening tools in GLIM diagnosis, and establish machine learning (ML) models to predict malnutrition in CRC patients without weight loss information. MethodsThis multicenter cohort study enrolled 4487 CRC patients. The capability of GLIM diagnoses combined with four screening tools in predicting survival probability was compared by Kaplan-Meier curves, and the most accurate one was selected as the malnutrition reference standard. Participants were randomly assigned to a training cohort (n=3365) and a validation cohort (n=1122). Several ML approaches were adopted to establish models for predicting malnutrition without weight loss data. We estimated feature importance and reserved the top 30% of variables for retraining simplified models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess and compare model performance. ResultsNRS-2002 was the most suitable screening tool for GLIM diagnosis in CRC patients, with the highest hazard ratio (1.59; 95% CI, 1.43-1.77). A total of 2076 (46.3%) patients were malnourished diagnosed by GLIM combined with NRS-2002. The simplified random forest (RF) model outperformed other models with an AUC of 0.830 (95% CI, 0.805-0.854), and accuracy, sensitivity and specificity were 0.775, 0.835 and 0.742, respectively. We deployed an online application based on the simplified RF model to accurately estimate malnutrition probability in CRC patients without weight loss information (https://zzuwtt1998.shinyapps.io/dynnomapp/). ConclusionsNutrition Risk Screening 2002 was the optimal initial nutritional risk screening tool in the GLIM process. The RF model outperformed other models, and an online prediction tool was developed to properly identify patients at high risk of malnutrition.

Automated Classification of COVID-19 Chest X-ray Images Using Ensemble Machine Learning Methods

This study delves into the efficacy of ensemble machine learning techniques for classifying chest X-ray images into three distinct categories: Normal, COVID-19, and Lung Opacity. Employing the Random Forest Classifier and a rigorous k-5 cross-validation framework, we aimed to enhance diagnostic accuracy for one of the most urgent medical challenges today—rapid and reliable COVID-19 detection. The analysis revealed an average accuracy of 51%, with varying precision and recall across different folds. The F1-score remained consistently around 35%, indicating a need for improved balance between precision and recall. Visualizations such as performance metric trends and a confusion matrix provided further insight into the classifier's performance, highlighting a notable degree of misclassification. Despite moderate success in the automated classification of the images, our research illustrates the complexity of applying machine learning to medical imaging, especially in differentiating between diseases with overlapping radiographic features. The study’s findings emphasize the potential of machine learning models to support diagnostic processes and suggest the necessity of advanced pre-processing techniques and extended datasets for enhanced model training. The research contributes to the growing body of knowledge in computational diagnostics and underscores the importance of developing robust, accurate machine learning tools to aid in the global healthcare crisis precipitated by the pandemic.

Efficacy of Machine Learning Techniques in Diagnosing Urinary Tract Infections: A Study Utilizing a Philippine Clinical Dataset

This research delves into the potential of machine learning models, namely Support Vector Machine (SVM), XGBoost, and LightGBM, to enhance the diagnosis of Urinary Tract Infections (UTIs) based on a comprehensive dataset collected from a local clinic in Northern Mindanao, Philippines, spanning from April 2020 to January 2023. The study integrates clinical variables such as age, gender, and various urine test results including color, transparency, and the presence of substances like glucose, protein, and cells, to determine the most accurate diagnostic model. The dataset presented unique preprocessing challenges, such as converting infant ages into decimal numbers. The SVM with a linear kernel showed remarkable test accuracy of 98.25%, indicating its robustness in handling linear separability in the data. Meanwhile, XGBoost and LightGBM, both with optimal hyperparameter configurations, achieved comparable accuracies of 97.95%. These results underscore the significance of machine learning in medical diagnostics, particularly in settings where swift and reliable decision-making is crucial. Our findings suggest that while ensemble methods like XGBoost and LightGBM are powerful tools for complex datasets, a well-tuned SVM can provide superior accuracy, thus advocating for a data-centric approach in model selection.

Development of an integrated machine learning model to improve the secondary inorganic aerosol simulation over the Beijing–Tianjin–Hebei region

Secondary inorganic aerosols (sulfate, nitrate, and ammonium, SNA) are the key components of PM2.5 in China. Accurate and seamless SNA concentration data therefore are important for pollution controls and environmental health risk studies. However, the spatiotemporal characters and changes of SNA are still difficult to accurately estimate due to the shortages of SNA datasets with high accuracy and resolution, as well as the uncertainties in SNA simulations arising from air-quality numerical models. In this study, we developed an integrated model to improve hourly SNA simulations with 5 km spatial resolution over the Beijing–Tianjin–Hebei (BTH) region by integrating the random forest (RF) and Light Gradient Boost Machine (LGBM) using Stacked Generalization. Our model fuses Three-dimensional (3D) numerical simulations from the Weather Research and Forecast model (WRF) and Nested Air Quality Prediction Model System (NAQPMS) with observations of 16 sites from the BTH monitoring network. Three months’ data from January to March 2020 were employed to evaluate the model performance using the cross-validation (CV) method. The results showed that the integrated model provide more accurate simulations of SNA than the 3D numerical model does, with root mean square errors (RMSE) decreased by 33%, 45%, and 35%; correlation coefficient (R) increased by 61%, 28%, and 34%; and Taylor skill score (TSS) increased by 331%, 85%, and 65% for sulfate, nitrate, and ammonium respectively. Moreover, the integrated model showed higher evaluation criteria and more accurate spatiotemporal characteristic compared with the single machine learning (ML) model, especially in heavily polluted area. This study provides a new approach to improve SNA simulations and reveals the potential of ML models for improving aerosol modeling when observational data are scarce.

Cryptocurrency price prediction based on Xgboost, LightGBM and BNN

The valuation and prediction of cryptocurrency prices have become increasingly important in the financial market. Therefore, this study aims to focus on the selection and evaluation of machine learning models for cryptocurrency valuation. Thus, two types of machine learning models, gradient boosting trees (Xgboost and LightGBM) and neural networks, are compared to determine their effectiveness in generating features for cryptocurrency valuation. Additionally, correlation tests are conducted to identify the most suitable input variables for the models. The results demonstrate that the generated features have a significant impact on the accuracy of machine learning predictions for cryptocurrency prices. It highlights the potential of machine learning models in accurately predicting and evaluating the value of cryptocurrencies. Overall, the findings of this study contribute to the understanding of the role of machine learning in cryptocurrency valuation and provide valuable insights for investors and researchers. By leveraging machine learning techniques, investors can make informed decisions and develop effective investment strategies in the cryptocurrency market. This study contributes to cryptocurrency valuation research. Leveraging machine learning enables informed decisions and effective investment strategies. Furthermore, the findings inform the development of advanced machine learning models and algorithms for cryptocurrency valuation.

Machine Learning Analysis of Post-Operative Tumour Progression in Non-Functioning Pituitary Neuroendocrine Tumours: A Pilot Study.

Post-operative tumour progression in patients with non-functioning pituitary neuroendocrine tumours is variable. The aim of this study was to use machine learning (ML) models to improve the prediction of post-operative outcomes in patients with NF PitNET. We studied data from 383 patients who underwent surgery with or without radiotherapy, with a follow-up period between 6 months and 15 years. ML models, including k-nearest neighbour (KNN), support vector machine (SVM), and decision tree, showed superior performance in predicting tumour progression when compared with parametric statistical modelling using logistic regression, with SVM achieving the highest performance. The strongest predictor of tumour progression was the extent of surgical resection, with patient age, tumour volume, and the use of radiotherapy also showing influence. No features showed an association with tumour recurrence following a complete resection. In conclusion, this study demonstrates the potential of ML models in predicting post-operative outcomes for patients with NF PitNET. Future work should look to include additional, more granular, multicentre data, including incorporating imaging and operative video data.

Use of machine learning models for the prognostication of liver transplantation: A systematic review.

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models. To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws. Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI. This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

Comparative analysis of machine learning models for predicting dielectric properties in MoS2 nanofiller-reinforced epoxy composites

This research investigates the dielectric properties of nano epoxy composites by incorporating various concentrations of MoS2 into epoxy resin. The study explores the impact of synthesized nanoparticles on undoped epoxy composites, specifically focusing on their potential applications in dielectric materials. The experimental synthesis and characterization of nanoepoxy composites typically involve time-consuming and expensive methods. This study compares five machine learning (ML) models—random forests, decision trees, extra trees, XGBoost, and gradient boosting—in order to predict the frequency-dependent dielectric constants in these composites under different nanofiller variations in order to address this challenge. To ensure robust model performance, training is carried out on different subsets of the dataset, ranging from 60% to 30%, while the remaining portions are reserved for testing purposes (40% to 70%). The main objective of the study is to assess the performance of each regressor technique using metrics such as adjusted R2 score, MSE, RMSE, and MAE, in which the ET regressor excels. The ET method demonstrates exceptional performance, achieving an adjusted R2 value of 0.9977 and 0.9912 for target variables ε′ and ε′′, respectively when tested with a size of 0.4. The findings underscore the potential of ML models for precise and efficient prediction of frequency-dependent dielectric constants of nanoepoxy composites with various concentrations of nanofillers, offering an alternative to time-consuming and expensive laboratory work.

Human Activity Recognition System with Smartphones Using Machine Learning

Abstract: Human activity recognition (HAR) is a crucial task in the field of physical activity monitoring. It involves the identification of a person's activities and movements using sensors. The accuracy of HAR systems plays a significant role in enhancing physical health, preventing accidents and injuries, and improving security systems. One of the most common approaches to HAR is using smartphones as sensors. However, smartphones have limited processing power and battery life, which can impact the performance of HAR systems. To overcome these challenges, we can apply different machine learning models and compare their performances. We begin by selecting a standard dataset, the HAR dataset from the Machine Learning Repository. We then apply several Machine learning models, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).To compare the performance of these models, we train and test each model on the HAR dataset. We also select the best set of parameters for each model using grid search. Our results show that the Support Vector Machine (SVM) performed the best (average accuracy 96.33%), significantly outperforming the other models. We can confirm the statistical significance of these results by employing statistical significance test methods.The SVM model demonstrated superior performance in recognizing human activities. This highlights the potential of machine learning models in revolutionizing the field of HAR. However, further research is needed to address the challenges of limited processing power and battery life in smartphones and to explore other potential applications of HAR systems.

Enhancement in the safety and reliability of Pressurized Water reactors using Machine Learning approach

The increasing interest in artificial intelligence and automation within the nuclear industry stems from the hope of elevating the safety and reliability of nuclear power plants by minimizing the impact of human errors. This paper explores the capabilities of machine-learning based fault detection and diagnosis (FDD) models in accurately classifying transient events in a nuclear power plant using the Classification Learners Application from MATLAB. The scope of the research is narrowed to the identification of transient events of the same nature occurring at various locations within the plant. To accomplish this, the Generic Pressurized Water Reactor (GPWR) from the Western Services Cooperation (WSC) was used to simulate 14 transients in the primary system and 10 transients in the secondary system, resulting in two distinct datasets of 108,000 and 79,200 observations. Nine types of classifiers, including Decision Trees, Discriminant Analysis, Support Vector Machines, Logistic Regression, Nearest Neighbors, Naive Bayes, Kernel Approximation, Ensembles, and Neural Networks, with a total of 33 predefined models, were trained and validated. The K-Nearest Neighbors model achieved the highest accuracy of 97% in the primary system, while the Efficient Linear Discriminant and Logistic Regression models achieved the highest average accuracy of 99% in the secondary system. The assessment of the top-performing models was conducted through a comprehensive analysis employing key classification model evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and the F1 score. In an effort to optimize the models, the study integrated feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm. Additionally, the investigation explored different validation schemes, including both holdout and k-fold cross-validation schemes, resulting in a substantial reduction in training time without significantly compromising the overall accuracy of the models. The optimized models in this study demonstrated remarkable prediction accuracies, consistently exceeding 94%. Despite the complexity of the models and the intricacies involved in the training process, the training times ranged from 10 to 1800 s, reflecting the efficiency and robustness of the implemented optimization techniques. The results of this study underscore the potential of machine learning models in identifying transient events in nuclear reactors, showcasing their promising capabilities while maintaining low computational and execution costs. This suggests their efficacy in optimizing safe and efficient operational practices within nuclear facilities.

Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

Machine Learning-Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts.

Given the additional risk of suicide-related behaviors in adolescents with allergic rhinitis (AR), it is important to use the growing field of machine learning (ML) to evaluate this risk. This study aims to evaluate the validity and usefulness of an ML model for predicting suicide risk in patients with AR. We used data from 2 independent survey studies, Korea Youth Risk Behavior Web-based Survey (KYRBS; n=299,468) for the original data set and Korea National Health and Nutrition Examination Survey (KNHANES; n=833) for the external validation data set, to predict suicide risks of AR in adolescents aged 13 to 18 years, with 3.45% (10,341/299,468) and 1.4% (12/833) of the patients attempting suicide in the KYRBS and KNHANES studies, respectively. The outcome of interest was the suicide attempt risks. We selected various ML-based models with hyperparameter tuning in the discovery and performed an area under the receiver operating characteristic curve (AUROC) analysis in the train, test, and external validation data. The study data set included 299,468 (KYRBS; original data set) and 833 (KNHANES; external validation data set) patients with AR recruited between 2005 and 2022. The best-performing ML model was the random forest model with a mean AUROC of 84.12% (95% CI 83.98%-84.27%) in the original data set. Applying this result to the external validation data set revealed the best performance among the models, with an AUROC of 89.87% (sensitivity 83.33%, specificity 82.58%, accuracy 82.59%, and balanced accuracy 82.96%). While looking at feature importance, the 5 most important features in predicting suicide attempts in adolescent patients with AR are depression, stress status, academic achievement, age, and alcohol consumption. This study emphasizes the potential of ML models in predicting suicide risks in patients with AR, encouraging further application of these models in other conditions to enhance adolescent health and decrease suicide rates.

Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data.

To develop and compare different AutoML frameworks and machine learning models to predict premature birth. The study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. Three Automatic Machine Learning (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample (N = 536,971). The area under the curve (AUC) and training times were used to assess the performance of the prediction models, and feature importance was computed via permutation-shuffling. The H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Auto-sklearn (median AUC: 0.820), and the median training time was the lowest for H2O AutoML (0.14 min), followed by AutoGluon (0.16 min) and Auto-sklearn (4.33 min). Among different types of machine learning models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XGBoost), stacked ensemble, and random forrest models had better predictive performance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Important features related to preterm birth included premature rupture of membrane (PROM), incompetent cervix, occupation, and preeclampsia. Our study highlights the potential of machine learning models in predicting the risk of preterm birth using readily available electronic medical record data, which have significant implications for improving prenatal care and outcomes.

Rethinking Dementia Risk Prediction: A Critical Evaluation of a Multimodal Machine Learning Predictive Model.

A recent study by Ding et al. explores the integration of artificial intelligence (AI) in predicting dementia risk over a 10-year period using a multimodal approach. While revealing the potential of machine learning models in identifying high-risk individuals through neuropsychological testing, MRI imaging, and clinical risk factors, the imperative of dynamic frailty assessment emerges for accurate late-life dementia prediction. The commentary highlights challenges associated with AI models, including dimensionality and data standardization, emphasizing the critical need for a dynamic, comprehensive approach to reflect the evolving nature of dementia and improve predictive accuracy.

Politicization as a Policy Instrument: China’s Politicized Policy Narrative of Environmental Protection and Control of Its Social Resonance

ABSTRACT Policy politicization is an instrument used by modern states to achieve policy goals. Although politicization can facilitate a policy’s implementation by elevating its priority in the bureaucratic system, it may also create opportunities for the public to question the regime’s legitimacy and to call for broader political reforms. Using empirical evidence from 20 years of official narratives and public intellectual writings on environmental protection, we demonstrate this dilemma of politicization. Our research yields generalizable conclusions not only for environmental policy but also for other policy areas and political contexts, and thus paves the way for a new line of inquiry into governance under authoritarian rule. Methodologically, we demonstrate the potential of machine learning models for supporting qualitative textual analysis and overcoming limitations in data availability.

MACHINE LEARNING FOR PREDICTING STUDENTS' ACADEMIC ACHIEVEMENT BASED ON LEARNING STYLE AND ACADEMIC RESULTS

Abstract: The objective of this study is to explore the viability of utilizing machine learning methods in forecasting students' academic success by examining their unique approaches to learning. The data was obtained by administering a questionnaire that employed the Motivated Strategies for Learning Questionnaires (MSLQ), along with the academic records of Semester 4 students from Jabatan Teknologi Maklumat dan Komunikasi (JTMK) Kuching Sarawak. After pre-processing the data and devising relevant features, we proceeded to train and test our machine-learning model. By employing Linear Regression, Decision Tree Regressor, Random Forest Regressor, Lasso, and Ridge, we were able to accurately anticipate students' performance based on their learning styles. Our study shows that the Random Forest and the Ridge have the most accuracy in predicting students' performance with MSE = 0.11, MAE = 0.22, and RMSE 0.32 for both models. The findings demonstrate the potential of machine learning models in accurately forecasting academic achievement, thereby offering valuable insights to educators who wish to personalize their teaching methods and intervention strategies. In summary, this study underscores the capacity of machine learning techniques in education to optimize learning outcomes and ensure the academic success of students.