This study examines the complex issue of specifically quantifying sensitive quantitative variables while ensuring respondents’ privacy and maintaining data integrity. The present study presents two novel optional stratified double response models (OSDRMs) that integrate simultaneously additive and subtractive frantically mechanisms to improve privacy protection while maintaining efficiency. The models utilize two scrambling variables for each response and employ stratified random sampling (SRS) to divide the population into similar subgroups (strata), ensuring that each subgroup is representative. In every stratum, simple random sampling with replacement (SRSWR) is employed, resulting in more precise estimates. The OSDRMs develop unbiased and viable estimates by assessing the mean as well as the sensitivity level for highly sensitive information, thereby reducing the likelihood of response bias and social desirability effects. The mathematical derivations and comprehensive simulation studies indicate notable enhancements in relative efficiency when contrasted with conventional randomized response methods. We investigated the suggested models in actual scenarios by conducting a cross-sectional survey in the Lahore, Faisalabad, and Sargodha districts of Punjab, Pakistan. The results demonstrate that OSDRMs are an effective, privacy-protecting technique for collecting sensitive data, resulting in improved precision and dependability of data. This strategy is significant for public health management because it promotes ethical data gathering while also improving monitoring and policy formulation in circumstances involving sensitive data.

Background: Precise measurement of sensitive public health outcomes is often limited by underreporting, stigma, and social desirability bias. These challenges can affect estimates of COVID-19 infection and vaccine hesitancy, leading to incomplete evidence for surveillance and public health decision-making. This study examined the use of privacy-preserving stratified randomized response models (SRRMs) to improve the estimation of sensitive health information in a population-based setting. Methods: We conducted a cross-sectional quantitative survey in Punjab, Pakistan, during June-August 2021. A total of 1,200 participants were recruited using simple random sampling with replacement within a stratified design, with equal allocation to urban ( n = 600) and rural ( n = 600) populations. Privacy-preserving SRRM-I and SRRM-II procedures were applied to estimate underreported outbreak cases and vaccine hesitancy while reducing response bias related to confidentiality concerns. Estimates were compared with directly reported responses, and precision was assessed using the percentage relative efficiency (PRE). Results: The empirical estimates reflect the June–August 2021 survey period in Punjab, Pakistan. Estimated outbreak prevalence was higher than directly reported prevalence in both urban and rural populations, indicating underreporting of infection. In urban areas, directly reported COVID-19 cases (10.5%) were lower than privacy-preserving estimates obtained using SRRM-I and SRRM-II (15.3% and 17.4%). In rural areas, directly reported cases (13.7%) were also lower than the corresponding estimates (16.7% and 19.5%). Vaccine hesitancy estimates were also higher under the privacy-preserving procedures (26.3% reported vs. 27.4% and 27.2%), although the difference was considerably smaller than for outbreak cases. All PRE values exceeded 100, indicating improved efficiency relative to the benchmark models. These findings suggest that conventional self-reporting underestimates sensitive public health outcomes, particularly where disclosure concerns are present. Conclusion: Privacy-preserving SRRM frameworks can improve the estimation of sensitive public health outcomes, including outbreak underreporting and vaccine hesitancy. The advantage of the two-stage design was most pronounced for the more sensitive outcome, indicating that the truthful-reporting parameter should be matched to the perceived sensitivity of the outcome under study. In settings where respondents are reluctant to disclose health-related information, such methods can strengthen surveillance data quality and support more reliable public health planning and policy decisions.

Cardiovascular diseases are the heart-related illnesses. If not treated, that often turns into serious and complex conditions that often result in the death of an individual. It is one of the top causes of death globally. The heart pumps blood throughout the body. This research focuses on predictive analysis and the determination of the severity of heart disease using machine learning algorithms. The datasets are selected then feature selection is the major step of preprocessing, which involves mechanisms to use impactful columns of the data set for experiments. Feature selection techniques include technical methods like the Pearson Correlation Coefficient, Relief, Minimum Redundancy Maximum Relevance, and Least Absolute Shrinkage and Selection Operator. Any of them generate results based on high relevancy and information gain. Choosing relevant features,due to the existence of labeled data, the classification technique is ideal. The supervised learning algorithms are applicable for this prospective. There also exists a proposed ensemble method algorithm. This approach results in an accuracy of 94.74%. The research is significant since it is helpful to determine early heart disease detection, which is an important aspect of early disease detection, which may save lives by early diagnosis in the future.