Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.

Background Globally hypertension ranks among the most significant causes of cardiovascular illnesses, with an increasing occurrence in low-income groups. In these areas, major risk factors include inadequate healthcare access, unhealthy diets, and sedentary lifestyles. Understanding the occurrence and determinants of hypertension in these settings is crucial for developing effective and practical approaches to its prevention and control. Objectives To determine the prevalence of hypertension and identify its associated risk factors among adults residing in a low-income community, in order to guide targeted prevention and management strategies. Methodology A cross-sectional study was conducted among 400 adults aged 18 years or older from a low-income community. Blood pressure measurements were obtained, and risk factors including age, gender, family history, obesity, smoking, and physical inactivity were assessed using structured interviews. Results Out of 400 patients, the mean age of the participants was 45.3 years (SD ± 10.2). Among the 400 adults, 40% had hypertension, with 22% having stage 1 and 18% having stage 2 hypertension. A significant association was found between hypertension and risk factors, including obesity (p < 0.05), physical inactivity (p < 0.01), and high salt intake (p < 0.001). Conclusion This low-income community has a significantly high prevalence of hypertension, for which obesity and physical inactivity, coupled with high sodium intake, are partly the drivers. To reduce the disease burden and enhance community health, implementing effective, preventive community-based strategies focused on lifestyle changes is necessary.