Predictive analytics in customer behavior: Unveiling economic and governance insights through machine learning

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

The integration of machine learning (ML) models with cloud computing has transformed the landscape of predictive analytics, offering scalable, efficient, and flexible solutions for organizations. Cloud platforms such as AWS, Google Cloud, and Microsoft Azure enable businesses to deploy and manage complex ML models without the need for extensive on-premise infrastructure. However, optimizing these ML models for performance and cost-efficiency in cloud environments presents unique challenges, including resource management, latency, scalability, and data security. This paper focuses on strategies to optimize machine learning models specifically for predictive analytics in cloud environments. It explores key techniques such as auto-scaling, model compression, and hyperparameter tuning, which are critical for improving the accuracy and speed of predictions while minimizing computational costs. The research also examines advanced tools such as containerization, serverless computing, and cloud-native services that further streamline the deployment and management of ML models. In the Indian context, where cloud adoption is growing rapidly, optimizing ML models is crucial for businesses across various sectors, including finance, healthcare, and e-commerce. By leveraging cloud-based ML solutions, Indian companies can enhance their predictive analytics capabilities, driving smarter decision-making and operational efficiency. This abstract presents an overview of how optimized machine learning models can unlock the full potential of predictive analytics in cloud environments, leading to better business outcomes. Through case studies and practical applications, this paper provides actionable insights into the best practices for optimizing ML models in a cloud-based setting.

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

As artificial intelligence (AI) becomes widespread, there is increasing attention on investigating bias in machine learning (ML) models. Previous research concentrated on classification problems, with little emphasis on regression models. This paper presents an easy-to-apply and effective methodology for mitigating bias in bagging and boosting regression models, that is also applicable to any model trained through minimizing a differentiable loss function. Our methodology measures bias rigorously and extends the ML model's loss function with a regularization term to penalize high correlations between model errors and protected attributes. We applied our approach to three popular tree-based ensemble models: a random forest model (RF), a gradient-boosted model (GBT), and an extreme gradient boosting model (XGBoost). We implemented our methodology on a case study for predicting road-level traffic volume, where RF, GBT, and XGBoost models were shown to have high accuracy. Despite high accuracy, the ML models were shown to perform poorly on roads in minority-populated areas. Our bias mitigation approach reduced minority-related bias by over 50%.

Permeability is a crucial parameter in reservoir engineering, as it determines how easily fluids can flow through subsurface rocks. Traditional methods of measuring permeability are expensive, error-prone and time-consuming, which has led to the development of machine learning models that can predict permeability based on well-log data. This study aims to determine the most effective machine learning models for predicting permeability based on available well-log data. The study covers a detailed explanation of the data-gathering and pre-processing techniques used. Four input features were used in the models, namely gamma ray (GR), bulk density (RHOB), neutron porosity (NPHI), and resistivity (RT). Six different models were trained and evaluated based on their performance, namely linear regression, support vector machine, decision trees, gradient boosting, multi-layer perceptron, and random forest models. The models were evaluated based on their Mean Squared Error and RSquare values. Our results showed that the random forest model outperformed the other models, achieving a Mean Squared Error (MSE) value of 0.88. The multi-layer perceptron and gradient boosting models also showed promising results, achieving MSE values of 0.81 each. These findings suggest that machine learning models can be a powerful tool for predicting permeability from well-log data, and the random forest model, in particular, holds great promise for future modelling efforts in this area.

Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew's Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features' importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.

Computing nodes in Edge Computing environments share unlimited data. Such data are exploited to locally build Machine Learning (ML) models for applications such as predictive analytics, exploratory analysis, and smart applications. This edge node-centric local learning reduces the need for data transfer and centralization, which is affected by different factors such as data privacy, data size, communication overhead, and computing resource limitations. Therefore, a collaborative learning fashion at the network edge has appeared as a promising paradigm that enables multiple distributed (edge) nodes to train and deploy ML models cooperatively without infringement of data privacy. Nevertheless, the variety, distribution and quality of data vary between edge nodes. Hence, selecting unsuitable edge nodes can have a negative impact on the ML model performances. We have devised (i) an intelligent node selection mechanism per analytics query based on the range of the availability of required training data at the edge and (ii) variants of collaborative learning processes engaging the most suitable nodes for models training and inference. We evaluate the efficiency of our selection mechanism and collaborative learning and provide a comparative assessment with other methods found in the literature using real data. The results showcase that our mechanism significantly outperforms baseline approaches and existing node selection mechanisms in distributed computing environments.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Background This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). Methods This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. Results There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0–83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. Conclusions In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

Social determinants of health (SDOH) play a critical role in the onset and progression of chronic kidney disease (CKD). Despite the well-established role of SDOH, previous studies have not fully incorporated these factors in predicting CKD in Type 2 diabetes patients. To bridge this gap, this study aimed to develop and evaluate the machine learning (ML) models that incorporate SDOH to enhance CKD risk prediction in Type 2 diabetes patients. Data were obtained from the 2023 Behavioral Risk Factor Surveillance System (BRFSS), a national survey that collects comprehensive health-related data from adults across the United States. Missing data were addressed using the K-nearest neighbor imputation method, and the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance class distributions. Potential predictive features were selected using correlation coefficient analysis. The dataset was partitioned into training (80%) and testing (20%) subsets, with a 3-fold cross-validation strategy applied to the training data. Seven ML models were developed for CKD risk prediction, including logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), eXtreme Gradient Boosting (XGBoost), and an artificial neural network (ANN). Model performance was evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUROC), precision, recall, F1 score, accuracy, and false positive rate. The study included 19,912 Type 2 diabetes patients (weighted sample size: 818,878), among whom 2,924 (weighted 13.92%) had CKD, and 16,988 (weighted 86.08%) did not. Over half of the CKD group (50.4%) were aged 65 or older. The proportion of female patients was higher in both groups, comprising 53.8% of the CKD group and 50.5% of the non-CKD group. Among the ML models evaluated, the RF model demonstrated the highest predictive performance for CKD, with an AUROC of 0.89 (95% CI: 0.88 - 0.90), followed by the DT model (0.84, 95% CI: 0.83 - 0.85) and XGBoost (0.83, 95% CI: 0.82 - 0.84). The RF model achieved an accuracy of 0.81 (95%CI: 0.81 - 0.81), a precision of 0.79 (95%CI: 0.79 - 0.79), a recall of 0.85 (95%CI: 0.85 - 0.85), and an F1 score of 0.82 (95%CI: 0.82 - 0.82). Additionally, the RF model exhibited strong calibration, reinforcing its reliability as a predictive tool for CKD risk in individuals with Type 2 diabetes. The study findings underscore the potential of ML models, particularly the RF model, in accurately predicting CKD among individuals with Type 2 diabetes. This approach not only enhances the precision of CKD prediction but also highlights the importance of addressing social and environmental disparities in disease prevention and management. Leveraging ML models with SDOH can lead to earlier interventions, more personalized treatment plans, and improved health outcomes for vulnerable populations. The online version contains supplementary material available at 10.1007/s40200-025-01621-9.

The evaluation of load-settlement behavior of piles is very crucial in meeting the serviceability criteria for pile analysis and design. The most reliable approach for estimating this behavior can be achieved by conducting pile load tests. However, due to the considerable expense and time requirement of such in-situ testing, the load-transfer methods have been used routinely in practice. In this paper, an alternative tree-based machine learning (ML) modeling is explored to predict the load-settlement behavior of axially loaded single piles from cone penetration test (CPT) data. Two variants of tree-based ML models, the random forest (RF) and gradient boosted tree (GBT), are developed in this study to estimate the load-settlement behavior of piles from CPT data (corrected cone tip resistance, qt, and sleeve friction, fs). A database of load-settlement curves of 64 static pile load tests and the corresponding CPT test data were compiled and used for the development of these ML models. The developed RF and GBT models are evaluated based on several statistical criteria. The load-settlement curves for six PLTs predicted using the developed RF and GBT models were compared with the measured data and the load-settlement curves predicted using the conventional load-transfer methods. The results demonstrated the great potential of tree-based ML (RF, GBT) models for predicting the load-settlement behavior of axially loaded piles from CPT data. The comparison clearly shows that the ML models outperformed the conventional load-transfer methods. Amongst the two ML models, the results show that the GBT model outperformed the RF model.

The objective of this document is to identify the most accurate machine learning (ML) models for predicting the value of a real estate property, based on a systematic literature review (SLR). It was conducted on research published between 2022 and 2023 that analyzed the accuracy of ML models in real estate valuation. Information was extracted regarding the ML models used, the databases employed, and the models highlighted for their accuracy. A variety of ML models used in real estate valuation were identified, including Random Forest (RF), XGBoost, Gradient Boosting Machine (GBM), Linear Regression (LR), and Lasso Regression. RF and LR models stood out as the most accurate in the analyzed research, showing that the accuracy of ML models varies depending on the database, property characteristics, and valuation context. It was concluded that ML models like RF and LR are promising tools for improving the accuracy of real estate valuation. Additionally, the choice of the best ML model depends on factors such as the database, property characteristics, and specific valuation objectives. Further research is needed on the application of ML in real estate valuation, considering aspects such as model interpretability and the real estate market context.

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

BackgroundThe large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making.MethodsML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python’s scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots.ResultsMulticenter external validation: ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC: TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data.Single-center internal validation: Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results.ConclusionsAlthough preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.

Systematic literature review of machine learning based software development effort estimation models