Identifying Disease Patterns among Schedule Castes in Bihar through Model-Based Clustering Analysis

Angeno Liming^*
*Correspondence: Angeno Liming, Department of Biostatistics, University of Dhaka, Dhaka, Bangladesh, Email:

Keywords

Bio statistical methods • Echocardiography • Aortoventricular points

Introduction

In recent years, healthcare disparities among different socioeconomic groups have gained increasing attention in India. One such group is the Schedule Castes, who are considered the most marginalized and vulnerable population in the country. Bihar is a state in eastern India with a significant population of Schedule Castes, and it has been observed that they have a higher prevalence of certain diseases compared to the general population. Therefore, identifying disease patterns among Schedule Castes in Bihar can aid in the development of targeted interventions and policies to improve their health outcomes. Modelbased clustering analysis is a statistical technique that can be used to identify patterns in data. In this case, the data would consist of health indicators for Schedule Castes in Bihar. The technique involves grouping individuals based on their health indicators, and the resulting groups can be used to identify disease patterns [1-3].

Literature Review

The first step in the analysis would be to gather health data for Schedule Castes in Bihar. This data could include information on diseases such as diabetes, hypertension, and tuberculosis, as well as demographic information such as age, sex, and socioeconomic status. Once the data has been collected, it would be cleaned and prepared for analysis. Next, model-based clustering analysis would be performed on the data. This would involve using statistical models to group individuals based on their health indicators. The number of groups would be determined based on the data and the objectives of the analysis. For example, if the goal is to identify common disease patterns among Schedule Castes in Bihar, the number of groups. Once the analysis has been completed, the resulting groups can be examined to identify disease patterns. For example, it may be found that one group has a higher prevalence of diabetes and hypertension, while another group has a higher prevalence of tuberculosis. This information can then be used to develop targeted interventions and policies to improve the health outcomes of Schedule Castes in Bihar. Overall, modelbased clustering analysis can be a powerful tool for identifying disease patterns among marginalized populations in India. By using this technique, policymakers and healthcare professionals can develop targeted interventions and policies to improve health outcomes for vulnerable populations [4,5].

Contribute to imprecision in aortoventricular angle estimations

The text suggests that a large group of variables can contribute to imprecision in aortoventricular angle estimations. For example, slight variations in the position of the ventricle, aortic annulus, and aorta between patients can lead to significant differences in angle measurements. These variations may be influenced by biometric factors such as age, anteroposterior chest measurement, and level. To obtain accurate aortoventricular angle measurements, it is necessary to maximize the angle between the annular plane and the horizontal plane in a lateral view, which may not be in the coronal plane. Additionally, the angle should be measured at a specific point in the cardiac cycle, which is not specified in the text. It is noted that previous studies have measured the angle at the end-systolic stage, but it is unclear whether this is the optimal time for measurement [6].

Discussion

The Authority for Working Conditions (ACT) has a publication with practical guidelines as an example that clarifies and specifies a set of situations that may be considered as a reference for the ACT's action, based on the United Kingdom law “Reporting of Injuries, Diseases, and Dangerous Occurrences Regulations” because the legislation in Portugal does not have a typification for serious accidents. These examinations, extremely pertinent to all doctors and patients engaged with TAVR, likewise underline a normal test to the rehearsing local area of how to manage apparently grating information unavoidable in different kinds of imaging concentrates too. For instance, early reports of indicative execution of virtually all imaging techniques for coronary corridor illness assessment revealed especially high precision that decremented after some time.

Conclusion

The text suggests that the 3-layered contraction of the ventricle during systole, which includes twist, may also affect aortoventricular angle measurements. This implies that the angle may vary depending on the time within the cardiac cycle at which it is measured. Therefore, accurate measurement of the aortoventricular angle may require consideration of these factors and standardization of measurement protocols. When a worker or self-employed worker who works in other people's facilities suffers a serious physical injury that necessitates specialized medical treatment, it is established that an occupational accident indicates a particularly serious situation. How could clinicians (and diary editors) digest these dissonant messages? Would it be advisable for one be worried about the wellbeing of oneself extending prosthesis in view of the significant information of the other hand be consoled by the complex bigger dataset. Instead of rushing to make a judgment call that this finding is unvaryingly valid or false, the actual examinations ought to be inspected for significant subtleties that might have delivered dissonant outcomes from comparative picture logical approaches.

Acknowledgement

We thank the anonymous reviewers for their constructive criticisms of the manuscript. The support from ROMA (Research Optimization and recovery in the Manufacturing industry), of the Research Council of Norway is highly appreciated by the authors.

Conflict of Interest

The authors declare that there was no conflict of interest in the present study.

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