Competitions

A Machine Learning and Evaluation Framework for Analyzing the Impact of Maternal Health on Infant Development Summary This study establishes machine learning models to analyze correlations between infant behavior characteristics, maternal physical and mental health indicators, and infant sleep quality, and proposes treatment strategies. Problem 1 Preprocessed infant behavior features and maternal health indicators. Designed hierarchical statistics for multiple variables: ANOVA for continuous variables and logistic regression for categorical variables. Conducted correlation analysis and multifactor ANOVA, finding significant relationships: Maternal age ↔ infant sleep patterns & behavior features Maternal gestation period ↔ infant wake-up frequency Maternal HADS score ↔ infant wake-up frequency, total sleep time, behavior features Maternal EPDS score ↔ infant wake-up frequency, total sleep time No significant effects were found for other indicators. Problem 2 Trained models using logistic regression, Random Forest, Neural Networks, and XGBoost. Selected XGBoost as the best-performing model (highest accuracy). Optimized model parameters using loss function minimization and cross-validation. Predicted the behavior types for the last 20 infant samples using the trained XGBoost model. Problem 3 Combined genetic algorithms with the XGBoost model from Problem 2 to generate treatment plans. Final treatment costs: Moderate type: 695 CNY Quiet type: 10,448 CNY Problem 4 Evaluated infant sleep quality using the CRITIC method. Established a comprehensive sleep quality ranking system using rank-sum ratio evaluation, classifying sleep quality as excellent, good, medium, or poor. Determined indicator weights with the CRITIC method. Trained a Random Forest model to associate comprehensive infant sleep quality with maternal health indicators, predicting sleep quality for the last 20 infant samples. Problem 5 Based on the evaluation and association models from Problem 4, calculated the initial sleep quality of infant #238. Applied the same approach as Problem 3, updating maternal indicators in the association model to generate a new treatment plan: Moderate type (sleep quality: excellent), minimum cost: 8,699 CNY Keywords: XGBoost, Genetic Algorithm, CRITIC Method, Rank-Sum Ratio Evaluation, Random Forest, Association Model ...

An Analysis of Sustainable Strategies for Property Insurance Introduction In this paper, I developed an LSTM model with an accuracy of 85% to predict future natural disasters using natural disaster data in Florida and California of the past 30 years. Applied neural network, linear regression, and deep learning models to assess the relationship between natural disasters and property damage. Summary In recent years, homeowners and insurance companies have faced significant crises, necessitating the development of comprehensive solutions to meet the needs of all stakeholders involved in the insurance industry. This paper presents an innovative approach to property insurance by introducing an insurance company’s property allocation model based on deep learning and LSTM and a market investment model utilizing regression analysis, as well as a community conservation building model employing grey correlation analysis. These models provide valuable insights and correlation analyses for the property-casualty insurance sector, promoting a more sustainable industry. ...