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14 pages, 1580 KiB

Open AccessArticle

A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

by Julio Jerison E. Macrohon, Charlyn Nayve Villavicencio, X. Alphonse Inbaraj and Jyh-Horng Jeng

Diagnostics 2022, 12(11), 2782; https://doi.org/10.3390/diagnostics12112782 - 14 Nov 2022

Cited by 12 | Viewed by 3433

Early risk tagging is crucial in maternal health, especially because it threatens both the mother and the long-term development of the baby. By tagging high-risk pregnancies, mothers would be given extra care before, during, and after pregnancies, thus reducing the risk of complications. In the Philippines, where the fertility rate is high, especially among the youth, awareness of risks can significantly contribute to the overall outcome of the pregnancy and, to an extent, the Maternal mortality rate. Although supervised machine learning models have ubiquity as predictors, there is a gap when data are weak or scarce. Using limited collected data from the municipality of Daraga in Albay, the study first compared multiple supervised machine learning algorithms to analyze and accurately predict high-risk pregnancies. Through hyperparameter tuning, supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Multilayer Perceptron were evaluated by using 10-fold cross validation to obtain the best parameters with the best scores. The results show that Decision Tree bested other algorithms and attained a test score of 93.70%. To address the gap, a semi-supervised approach using a Self-Training model was applied to the modified Decision Tree, which was then used as the base estimator with a 30% unlabeled dataset and achieved a 97.01% accuracy rate which outweighs similar studies. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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13 pages, 1661 KiB

Open AccessArticle

A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential Election

by Julio Jerison E. Macrohon, Charlyn Nayve Villavicencio, X. Alphonse Inbaraj and Jyh-Horng Jeng

Information 2022, 13(10), 484; https://doi.org/10.3390/info13100484 - 9 Oct 2022

Cited by 23 | Viewed by 10111

Abstract

With the increasing popularity of Twitter as both a social media platform and a data source for companies, decision makers, advertisers, and even researchers alike, data have been so massive that manual labeling is no longer feasible. This research uses a semi-supervised approach to sentiment analysis of both English and Tagalog tweets using a base classifier. In this study involving the Philippines, where social media played a central role in the campaign of both candidates, the tweets during the widely contested race between the son of the Philippines’ former President and Dictator, and the outgoing Vice President of the Philippines were used. Using Natural Language Processing techniques, these tweets were annotated, processed, and trained to classify both English and Tagalog tweets into three polarities: positive, neutral, and negative. Through the Self-Training with Multinomial Naïve Bayes as base classifier with 30% unlabeled data, the results yielded an accuracy of 84.83%, which outweighs other studies using Twitter data from the Philippines. Full article

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16 pages, 3592 KiB

Open AccessArticle

Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

by Charlyn Villavicencio, Julio Jerison Macrohon, X. Alphonse Inbaraj, Jyh-Horng Jeng and Jer-Guang Hsieh

Information 2021, 12(5), 204; https://doi.org/10.3390/info12050204 - 11 May 2021

Cited by 146 | Viewed by 20970

Abstract

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines. Full article

(This article belongs to the Special Issue News Research in Social Networks and Social Media)

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