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The life insurance industry plays a strategic role in the national financial system, not only as a provider of protection against life risks such as premature death or critical illness, but also as an instrument of long-term fund accumulation. Increased public awareness of the importance of risk protection has driven significant growth in the number of active policies. This condition has a direct impact on the risk exposure of claims that must be carefully managed by insurance companies. One of the main challenges in risk management is to accurately estimate the number of claims in a certain period, to support premium setting, technical reserve planning, and maintain the company's financial stability. This study aims to examine the use of Poisson regression model in estimating the frequency of life insurance claims based on the number of active policies in life insurance company. The data used is simulative and represents an exponential relationship between the number of policies and claims. The model is analyzed using the Maximum Likelihood Estimation (MLE) approach and evaluated through goodness-of-fit indicators such as deviance, Pearson chi-square, log-likelihood, and Mean Squared Error (MSE). The results of the analysis show that the Poisson regression model can capture the significant relationship pattern between the number of active policies and claims, and provide accurate prediction results. Thus, Poisson regression is proven to be a relevant and applicable statistical method in supporting strategic decision-making in insurance companies, especially in the context of data-driven risk management.

. The Framingham Risk Score (FRS) assesses coronary heart disease (CHD) risk and predicts acute coronary events. Metabolic markers like LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio play critical roles in atherosclerosis and cardiovascular risk. Elevated LDL cholesterol, fasting blood glucose, and uric acid contribute to plaque formation, inflammation, and vascular damage, while high triglycerides and low HDL cholesterol exacerbate atherogenesis. This study explores the relationship between these markers and FRS to enhance CHD risk prediction and support targeted cardiovascular interventions. This study analyzed LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and TG/HDL ratio with Framingham Risk Score in 85 participants, excluding those with incomplete data or chronic illnesses. The analysis found significant correlations between metabolic parameters and the 10-year myocardial infarction risk. LDL cholesterol, triglycerides, and uric acid showed moderate positive associations with cardiovascular outcomes, while the triglyceride-to-HDL ratio and fasting blood glucose had weaker but significant correlations. These findings highlight lipid profiles and metabolic markers as key contributors to cardiovascular risk. This study highlights significant correlations between LDL cholesterol, fasting blood glucose, uric acid, triglycerides, and the triglyceride/HDL ratio with 10-year cardiovascular risk. These findings emphasize the importance of lipid profiles, glycemic control, and metabolic markers in predicting coronary outcomes and guiding targeted preventive interventions for improved cardiovascular risk management.

Thyroid cancer is the most common endocrine malignancy, with a steadily increasing incidence rate. Although the overall survival rate is relatively high, the risk of recurrence after definitive treatment such as Radioactive Iodine (RAI) therapy remains a significant clinical challenge. Predicting recurrence risk is crucial for optimizing monitoring strategies and interventions. With advances in technology, machine learning (ML) approaches are increasingly utilized to support medical predictions, including the recurrence of thyroid cancer. This study aims to evaluate the performance of four classification algorithms—Logistic Regression, XGBClassifier, Random Forest Classifier, and Voting Classifier—in predicting thyroid cancer recurrence using the Thyroid Cancer Recurrence After RAI Therapy dataset, which consists of 383 patient records and 13 key clinical attributes. The evaluation was conducted using accuracy, precision, recall, F1-score, and area under the curve (AUC) metrics. The results show that the XGBClassifier is the best-performing model with an accuracy of 97.4% and an AUC of 0.95, demonstrating superior performance in handling the minority class. This research is expected to contribute to the development of more effective machine learning–based clinical decision support systems for predicting thyroid cancer recurrence after therapy.

The increasingly advanced era is certainly followed by various developments in the fields of economy, social, culture and even technology. Technological progress in various countries can be seen from the existence of a technology called AI. Artificial Intelligence or AI is an artificial intelligence technology that has the ability to solve problems like humans. In practice, AI will simulate human intelligence with the ability to recognize images, write and even make predictions based on data. Lately, AI has begun to be misused by individuals who feel they are benefiting. In Indonesia, the misuse of AI technology has been rampant where perpetrators use AI for fraud. An act that uses technology as the main weapon of crime is included in the category of cyber crime. The rampant case of cyber crime in Indonesia is the act of voice imitation or voice cloning. The perpetrators in committing their crimes usually use a technological intermediary that can change their voice so that it is similar to the voice of relatives, friends, family or important people or public figures recognized by the victim. Voice imitation or known as voice cloning is the ability to imitate a voice that is similar to the original voice. Voice cloning using AI technology is an imitation of the human voice with an extraordinary level of accuracy of similarity, both in intonation, tone, and also voice patterns. The law on information and electronic transactions contains various regulations regarding information and electronic transactions, but the law does not explain in detail the misuse of AI in voice cloning because there are still limitations in the ITE law regarding criminal acts of voice cloning fraud and until now criminal acts of voice cloning are still rampant.

In geotechnical engineering, building robust structures is crucial to ensure the bearing capacity of structures against external forces, so making sure soil strength and unreliable build cost and duration prediction are also very important and preliminary aspects of any construction project. Therefore, in this first-of-its-kind modern examine, the capability of various artificially intelligent (AI)-based models toward reliable forecasting and estimation of preliminary construction expenses, duration, and strength at shear is explored. First, background information about the revolutionary artificial intelligence (AI) technique along with its many distinct models ideal for geotechnical and building engineering problems is presented, The use of AI-based models in the literature for the aforementioned construction and maintenance applications is discussed in a number of current works, together with their benefits, drawbacks, and future directions. Several important input elements that significantly affect the preliminary price of construction, construction time, and soil's shear strength estimation are listed and given through analysis. Finally, some obstacles to employing AI-based models for precise forecasts in these applications are discussed, along with elements influencing the problems with cost overruns. Thus, this work can help civil engineers make effective use of artificial intelligence (AI) to solve difficult and risky tasks. It can also be used to Internet of Things (IoT) environments for self-learning applications like smart architectural health-monitoring systems

Given the increasing prevalence of mental health challenges in digital work settings, especially among IT remote workers, early detection mechanisms have become critically important. This study aims to improve the prediction accuracy of mental health conditions among IT remote workers by integrating feature engineering techniques within machine learning models. Five algorithms consisting of Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes were evaluated. The Random Forest model achieved the best performance, with 83% accuracy, 83% precision, 100% recall, and a 90% F1-score, followed closely by Logistic Regression with 82% accuracy. Nevertheless, the results demonstrate the feasibility of applying machine learning to support the early detection of mental health risks, offering a strong foundation for future research in predictive analytics and the development of intelligent support systems within digital work environments.

Health risk stratification is crucial for preventive healthcare, yet existing models often rely on binary classification generalized disease prediction, neglecting personalized health indicators and graded risk levels. Many studies apply feature selection techniques like Relief and Univariate Selection without quantifying the weighted impact of features. To address these gaps, this study introduces a Big Data-driven Health Index (HI) framework using PySpark for scalable health risk stratification. The HI is computed as a weighted sum of health-related features using SHAP Analysis, XGBoost, Random Forest, and Correlation Analysis. PySpark enables efficient processing of large-scale health data, and individuals are classified into Low and High Risk. Optimal classification thresholds are determined using the Youden Index from the ROC curve to balance sensitivity and specificity. Personalized health recommendations are generated based on risk categories to guide preventive interventions. Performance evaluation reveals that Correlation Analysis achieves 100% precision and 98.90% recall, outperforming other methods. SHAP prioritizes recall but has low precision, while XGBoost and Random Forest improve precision but struggle with recall. By leveraging Big Data techniques with PySpark, this study enhances computational efficiency, scalability, and classification accuracy, addressing prior research limitations and providing a robust data-driven approach to personalized health monitoring.

Travel insurance provides financial protection for individuals during their trips, both domestically and internationally. With the increasing demand for travel insurance, insurance companies face challenges in efficiently managing claims. This study aims to develop a predictive model to classify whether an insurance policy will be claimed based on historical customer and transaction data. This research utilizes a dataset containing various features related to travel and policyholders, such as agent type, distribution channel, insurance product, travel duration, and premium amount. The methods used include data exploration, feature processing, and the application of machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. Experimental results indicate that the XGBoost model performs the best, achieving the highest accuracy compared to other models. With this predictive model, insurance companies can optimize claim evaluation processes, reduce fraud risks, and improve operational efficiency in handling travel insurance claims.

Customer churn is a major challenge in e-commerce, directly affecting revenue and profit. This study aims to develop a machine learning model using XGBoost to predict churn probability. To handle class imbalance, SMOTE was applied as a resampling method, and hyperparameter tuning was performed to enhance performance. The model was evaluated using the F2-score, prioritizing recall while maintaining precision. The results show that the XGBoost model with SMOTE achieves strong performance, with an F2-score of 0.849 on the tuned test data. This model can help businesses identify at-risk customers early, enabling proactive retention strategies.

Early detection and prediction of preeclampsia are crucial to prevent severe complications and ensure timely interventions, Specific ECG patterns, including PR segment, Q wave duration and amplitude, ST segment, U wave, and sinus rhythm were under study for their potential indicators of preeclampsia. This study aims to investigate the predictive role of these ECG patterns in preeclamptic pregnant women in the third trimester of pregnancy. Fourty (40) consenting pregnant women were recruited from St. Philomina Catholic Hospital, Edo State, Nigeria. These subjects consisted of  twenty (20) normotensive  and twenty (20) preeclamptic pregnant women in their  third trimester of pregnancy. After the subjects were  identified and recruited into the study, they were taken to the laboratory where their vital signs was taken and their ECG patterns recorded with ECG machine. Data obtained from this study were analysed using Graph Pad Prism 9. Results generated were expressed as mean ± SEM and a P-value of ≤ 0.05 were considered statistically significant. results from this present study show no significant differences were observed in the P-R segment, R-R interval, Q wave duration, Q wave amplitude The study underscores the multifactorial nature of cardiovascular changes in preeclampsia and highlights the potential of ECG parameters in aiding early detection, risk stratification, and management of the condition, despite  parameters showing no significant differences. However, PR Segment, Q Wave duration and amplitude, ST Segment , U wave and Sinus rhythm cannot be used to predict preeclampsia  

This study explores the application of machine learning algorithms, specifically Linear Regression and Decision Tree Regressor, for predicting student academic performance using academic grade data from Kaggle. The analyzed factors include attendance, assignment grades, midterm exam grades, and final exam grades. The research methodology encompasses data collection, preprocessing, model development, training, and validation. This study contributes to the field of educational data analytics by demonstrating how machine learning can provide actionable insights into students' learning patterns and academic outcomes. The findings emphasize the effectiveness of Linear Regression for linearly distributed data and Decision Tree Regressor for capturing complex, non-linear relationships. The implications of this research suggest that machine learning models can assist educators in identifying key factors influencing student performance, enabling targeted interventions to enhance learning outcomes. Future research should explore larger, more diverse datasets and incorporate ensemble methods, such as Random Forest or Gradient Boosting, to improve model generalization and prediction accuracy. Additionally, integrating socio-economic and psychological factors could provide a more holistic perspective on academic achievement.

Predicting credit worthiness is an important step for banks to reduce the risk of bad credit. This research compares the performance of four classification algorithms, namely SVM, Naïve Bayes, Random Forest and Decision Tree using simulated datasets. The results obtained on the metrics of accuracy, precision, recall, F1 score, and AUC-ROC, show that Decision Tree has the best performance with 42.5% accuracy, 48.3% precision, 47.5% recall, 47.5% F1 score, and AUC 0.60, indicating its ability to is in differentiating credit worthiness. Random Forest achieved an accuracy of 37.5% and an AUC of 0.493, while Naïve Bayes had the lowest accuracy with an accuracy of 27.5% and an AUC of 0.425. SVM gives better results than Naïve Bayes but is still inferior to Decision Tree. This research recommends implementing a Decision Tree as the main model with optimization through hyperparameter tuning, adding relevant features, and handling data accounting. These results are expected to support banking decision making more effectively and efficiently.

This study aims to determine the forecasting of stock return volatility of energy companies listed on the Indonesian Sharia Stock Index (ISSI) using the ARCH/GARCH method. This study uses purposive sampling method and uses secondary data in the form of daily stock returns from January 2022 to June 2024 on 10 selected stocks. Data processing is done using Stata software. The results showed that of the 10 selected stocks, only 6 stocks, namely BYAN, ADRO, GEMS, PTBA, AKRA, and BSSR, were suitable for analysis using the ARCH/GARCH model. Meanwhile, PGAS, ITMG, PTRO, and HRUM do not show ARCH effect or do not contain heteroscedasticity. Statistical evaluation of volatility prediction shows that the selected models provide good predictions. Among the six stocks analyzed, ADRO, PTBA, and BSSR show high volatility, while BYAN, GEMS, and AKRA show low volatility. Therefore, investors should consider investment risk when evaluating stocks with different levels of volatility.

Estimating the effort, time, and cost needed to build a software project is an important task in software engineering. Estimating software prior to development can help to reduce risk and improve the project success rate. Researchers have developed numerous traditional and machine learning models to estimate software effort, but it has always been difficult to estimate effort precisely. This paper presents a predictive model based on artificial neural networks namely ANNs to predict the software effort. The NASA dataset is applied to construct the proposed model. The system was trained using 50 data points, and the remaining 10 were used for testing. It was concluded that the ANN approach could estimate the software effort with high accuracy. A comparative study with other published equations was also performed, and it was found that ANN had less error and produced better results than other existing methods.