No abstract

This study aimed to find a model to forecast monthly measles immunization coverage using Autoregressive Integrated Moving Average (ARIMA). The monthly registered data for measles immunization coverage from January 2014 to December 2018 were used for the development of the model. The best model with the smallest Normalized Bayesian Information Criterion (BIC) of 8.673 is ARIMA (0, 1, 0). ARIMA (0, 1, 0) was used to forecast the monthly measles immunization coverage for the next 36 months from January 2018 to December 2020. The results obtained prove that this model can be used for forecasting future immunization coverage and will help decision-makers to establish strategies, priorities, and proper use of immunization resources.

The State Universities and Colleges (SUCs) in the Philippines have established a model of Gender and Development (GAD) tools. They have different activities but lack of organized data sources with particular data needed for gender analysis like the sex-disaggregated data. GAD data is very important in gender analysis to enable the GAD Focal Point System to have the basis for gender issues and concerns. In this paper, the authors present information technology-based solution where the GAD Focal Point System has basis for gender analysis and proposed undertakings using a classification system like decision tree algorithm. The approach is better for discovering relevant solutions in improving university programs and activities to achieve the goal of gender equality.

This study aimed to find a suitable model for forecasting the appropriate stock of vaccines to avoid shortage and oversupply. The Auto-Regressive Integrated Moving Average (ARIMA) and Multilayer Perceptron Neural Network (MLPNN) models were used for forecasting time series data. The monthly vaccination coverage was used to develop the models from January 2014 until December 2019. The dataset consists of 72 months of observation, the 60 months of data are used for model fitting from January 2014 to December 2019, and the remaining 12 months of data from January 2019 to December 2019 are used to test the accuracy of the forecast. The most suitable forecast model was selected based on the lowest Root Mean Square Error (RMSE) value and the Mean Absolute Error (MAE). The analytical result shows that the MLPNN model outperformed the ARIMA model in forecasting monthly demand for vaccines. The results will help policymakers improve the proper use of vaccination resources.

Tuberculosis remains an important problem in public health that threatens the world, including the Philippines. Treatment relapse continues to place a severe problem on patients and TB programs worldwide. A significant reason for the development of decline is poor compliance with medical treatments. The objectives of this research are to generate a predictive data mining model to classify the treatment relapse of TB patients and to identify the features influencing the category of treatment relapse. The TB patient dataset is applied and tested in decision tree J48 algorithm using WEKA. The J48 model identified the three (3) significant independent variables (DSSM Result, Age, and Sex) as predictors of category treatment relapse.