{'fraction_non_alphanumeric': 0.02252252252252252, 'fraction_numerical': 0.003861003861003861, 'mean_word_length': 5.244979919678715, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of the Elderly's Internet Accessed Time using XGB Machine Learning Model for Solving the Level of the Information Gap of the Elderly

This study aims to construct machine learning models to predict the elderly's internet-accessed time. These models can resolve the information gaps in the present and future by analyzing information use factors such as internet access and mobile device usability. We analyzed 2,300 adults 55 years of age and older who participated in the national survey. This study followed a pipeline of five steps: primary data selection, data imputation to process missing data, feature ranking to identify most important features, machine learning algorithms to develop classifier models, and model evaluation. We applied the Extremely Randomized Trees classifier (Extra Tree) model, the Random Forest classifier (RF) model, and the Extreme Gradient Boosting classifier (XGB) model to look for feature ranking, then select feature importance. All classification models used the accuracy score to calculate the effect. In our study, the most accurate model for predicting the Internet access time of the elderly was the XGB model. The evaluation scores of the XGB machine learning model are very positive and bring high expectations. To solve the information gap of the elderly problem, we can use these effective models to predict the elderly object. Then, we can give some solutions to help them in a society with a strong information technology base. Keywords—Information gap; machine learning; prediction model; elderly