Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

A series of comprehensive focus group discussions with active firefighters revealed that inconsistencies in incorporating a rigorous physical training program during work hours contributed significantly to the challenges of maintaining functional fitness, particularly strength. This article outlines a process for identifying the specific exercises required to enhance strength, drawing on the expertise and experience of physical trainers. Modified nominal group technique (mNGT) sessions were conducted to identify and rank exercises for five functional strengths (i.e., pushing, pulling, lifting, carrying, and dragging). A total of six physical trainers from the firefighter academy were interviewed to 1) identify the skeletal muscles involved in the functional movements using a visual aid; 2) generate ideas for exercises via brainstorming; 3) privately rank the displayed list of exercises for each muscle group involved in each functional movement; and 4) design a physical training programme for beginner, intermediate and advanced levels by manipulating loads. Males dominated the expert group (66.7%) and had a mean of 11.50 (SD 5.20) years of experience in physical training. The mNGTs yielded three top-ranking exercises for each functional strength: 1) Pushing: the push-up, sit-up, and jumping jack; 2) pulling: the push-up, jumping jack, and sit-up; 3) lifting: the jumping jack, push-up, and jumping squat; 4) carrying: the push-up, plank, and sit -up; and 5) dragging: the jumping jack, sit-up, and jumping squat. Then each exercise was designed for the beginner, intermediate, and advanced levels by manipulating loads (i.e. additional weight or number of repetitions). This study demonstrated that mNGT is an effective tool for identifying the three top-ranking exercises that address firefighter recruits’ functional strength. The experts chose multi-joint exercises targeting agonist and antagonist skeletal muscle groups and mimicking movements of daily work life. The exercises, corroborated by previous empirical evidence, provide opportunities for common skeletal muscle groupings to be targeted simultaneously to increase cardiovascular adaptations over a shorter period. The physical exercise is now ready to be implemented for piloting purposes among firefighter recruits.

Colorectal cancer (CRC) carries a significant burden in most world regions. However, its screening uptake remains low. This study aimed to explore awareness and perspectives on CRC screening program in a multiethnic population and their preference for CRC screening decision aid. In-depth interviews were conducted until data saturation was reached. All interviews were audiotaped, transcribed verbatim, translated to English and analysed thematically using hybrid inductive and deductive approaches. 17 informants from three main ethnic groups (Malay, Chinese and Indian) with various levels of risk for developing CRC were recruited. Awareness on CRC screening program was found to be low. Majority of informants never heard of CRC screening program. Among 11 eligible informants, only five experienced CRC screening uptake. Thematic analysis of the transcripts yielded six major themes; knowledge on CRC, screening process, authority’s role, curability, willingness to screening and preference for decision aid, and theywere mapped onto the Health Belief Model. Specific multiethnic perspectives found included preference for traditional medicine coming from all ethnics, and reliance in God coming from Malay informants. Majority preferred short videos as CRC screening decision aid, in the form of animation and live-action screenplay. Exploration of perspective of CRC screening helps in producing impactful decision aids. Future efforts should focus on developing short videos that incorporate population’s perspectives and can be disseminated through electronic media.

Problem: In September 2024, an illegal toxic waste dumping incident along the Tebrau River in Johor State, Malaysia, raised widespread health concerns in Johor Bahru and Kulai districts. The pollution released a strong, unpleasant odour, resulting in acute symptoms among exposed individuals, including sore throat, dizziness and coughing. Context: The Tebrau River is a vital waterway supporting urban populations in Johor. This was not the first chemical pollution event in the region, as previous incidents, including the Kim Kim River crisis in 2019, highlighted the region’s vulnerability to such events. The involvement of multiple districts and agencies during the response presented challenges in coordination and data sharing. Action: The Johor Bahru District Health Office promptly deployed a rapid assessment team to assess the affected areas and implement both active and passive case detection. Community engagement targeted vulnerable populations, such as schoolchildren, to minimize exposure risks. Additional dumping sites identified along the Tebrau River prompted expanded surveillance and a state-level response to coordinate efforts across districts and all health-care facilities. Outcome: A total of 484 individuals were exposed to the pollution, 334 of whom developed symptoms related to chemical exposure. Timely public health actions consisted of actions to mitigate the impact. Health facilities were placed on high alert and community trust was maintained through proactive engagement. However, gaps in cross-district coordination and challenges accessing environmental data underscored areas for improvement. Discussion: This incident highlighted the importance of rapid assessment, cross-sector collaboration, community engagement and integrated data systems. It also showed that effective public health action is possible despite environmental data limitations. The strengthening of communication, standardized protocols and real-time data sharing will be critical to improving future chemical pollution events.

Abstract In Unani medicine, Bawl (urine) is recognized as a key diagnostic tool, with humoural imbalances assessed via parameters like color, consistency, sediment, clarity, froth, odor, and volume. This conceptual review explores how these classical diagnostic indicators may be contextualized alongside modern urinalysis markers (e.g., bilirubin, protein, ketones, and sedimentation) and examined through emerging artificial intelligence (AI) frameworks. Potential applications include ResNet-18 for color classification, You Only Look Once version 8 (YOLOv8) for sediment detection, long short-term memory (LSTM) for viscosity estimation, and EfficientDet for froth analysis, with standardized urine images/videos forming the basis of future datasets. Additionally, a comparative ontology is proposed to align Unani perspectives with diagnostic approaches in traditional Chinese medicine, encouraging cross-system integration. By synthesizing classical epistemology with computational intelligence, this review highlights pathways for developing AI-based decision support systems to promote personalized, accessible, and telemedicine-enabled healthcare.