Research

As the national university, we champion and support innovative research that addresses the country’s most pressing challenges.

10 Apr 2026

Using machine learning, study finds that key factors influencing poverty include country, whether people live in urban or rural areas, and their level of education

Relying only on income to determine poverty is not enough to fully understand someone’s well-being. Other factors, such as where...

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08 Apr 2026

Nearly 50% of isolates of Salmonella enterica from chicken meat collected from Metro Manila markets are resistant to three or more types of antibiotics

Antimicrobial resistance (AMR) occurs when medicines used to treat infections no longer work effectively, often due to their overuse and...

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07 Apr 2026

Plant-based milk alternatives have lower caloric, fat, and cholesterol content than dairy milk but often contain more sugar and sodium

The global market for plant-based milk alternatives (PBMiAs) is projected to grow steadily, reaching an estimated USD 30 billion by...

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06 Apr 2026

Many Filipinos lack full awareness of colorectal cancer, its risk factors, and screening options

Colorectal cancer (CRC) is one of the most common and deadliest malignancies in the Philippines. However, it can be prevented...

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01 Apr 2026

Gaps in a city’s urban landscape, also known as urban voids, have the potential to drive urban revitalization

Suburbanisation is the expansion and spatial reorganization of a growing metropolitan region. For Davao City, this phenomenon has notably damaged...

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31 Mar 2026

Study examines ethnomedicinal evidence on 97 plants traditionally used to treat urinary tract infections in the Philippines

The Philippines is one of the world’s 18 mega-biodiverse countries, accounting for two-thirds of global biodiversity and 70-80% of plant...

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30 Mar 2026

New method enables more reliable skin cancer detection by automatically predicting skin lesion types with greater accuracy

Skin cancer is one of the most common and dangerous cancers globally, but early detection can significantly reduce mortality rates....

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27 Mar 2026

The inclusion of bignay pomace reduces the estimated glycemic index of common local grains during digestion

Bignay [Antidesma bunius (L.) Spreng] is a fruit widespread in the Philippines. It is usually consumed in processed form as...

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26 Mar 2026

Researchers achieve an 86% response accuracy rate for a hand orthosis for stroke rehabilitation, using surface electromyography signals

Loss of control in gripping with the hand is a possible long-term effect of stroke. Recovery from this is possible...

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25 Mar 2026

Philippine island communities face unique healthcare challenges shaped by geographical isolation and disparities in technological access and literacy

In regions separated by water and challenging terrain, the healthcare journey involves not only crossing physical distances but also navigating...

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24 Mar 2026

The Spanish and Tausug versions of the landmark Sulu–Spanish Treaty of 1836 reflect different intentions

In the second half of the 1830s, several indigenous overlords in the Sulu–Mindanao–Borneo region began signing treaties, marking a new...

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20 Mar 2026

Scientists create the first draft genome of the endangered Visayan spotted deer, a species found only in the Philippines

The genus Rusa, native to South and Southeast Asia, lives in diverse habitats ranging from dense forests to grasslands. Among its...

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Research

Relying only on income to determine poverty is not enough to fully understand someone’s well-being. Other factors, such as where people live, their education, and the quality of their jobs, also matter. In the Philippines, it has been difficult to accurately identify who is truly in need, which has caused a gap between the people the programs are meant to help and those who actually benefit.

This study suggests using machine learning, specifically a Naïve Bayes classifier, to improve how poverty is assessed. By analyzing various social and economic factors, this approach could help policymakers make better decisions and use resources more effectively for poverty reduction programs. The Naïve Bayes model was compared to other machine learning models and performed better at predicting who might be living in poverty.

The study found that key factors influencing poverty include the country, whether people live in urban or rural areas, and their education level. The Naïve Bayes model correctly identified poverty status 69% of the time with new, unseen data. These findings show how machine learning can play an important role in tackling complex social issues like poverty.

The study aims to address the significant mismatch between the criteria used in the national targeting system for identifying the poorest families and the actual beneficiaries. Additionally, it seeks to fill a noticeable gap in the application of AI projects specifically targeting the United Nations Sustainable Development Goal of No Poverty by proposing an innovative approach to poverty assessment using machine learning techniques.

By employing a Naïve Bayes classifier and exploring various attributes influencing poverty, this research aims to inform policy decisions and optimize resource allocation for poverty alleviation programs. Furthermore, it contributes to the broader discourse on poverty assessment methods, advocating for a shift toward a more holistic, data-driven approach. By leveraging machine learning techniques, this study aspires not only to refine poverty classification but also to empower policymakers with tools that can adapt to the evolving dynamics of socioeconomic challenges.

Authors: Jamlech Iram N. Gojo Cruz (Institute of Computer Science, University of the Philippines Los Baños | National Graduate School of Engineering, University of the Philippines Diliman) and Prospero C. Naval (Department of Computer Science, University of the Philippines Diliman)

Read the full paper: https://ieeexplore.ieee.org/abstract/document/10674408