Sexually Transmitted Diseases in Females for Data Security and Privacy in 3D Modeling for Healthcare

Published: 10 November 2023| Version 1 | DOI: 10.17632/ty68672dnz.1
ankita gupta, Poonam Rani


Overview: This dataset comprises information collected from 450 females and is utilized for research in the field of Data Security and Privacy in 3D Modeling for Healthcare. The dataset is focused on the prevalence and analysis of sexually transmitted diseases (STDs) in females. It provides valuable insights into the demographics, behaviors, and health status of the study participants. Data Fields: The dataset includes the following key fields for each of the 450 females: A unique identifier assigned to each participant. Age: The age of the female participant, ranging from young adults to middle-aged individuals. Intimate Partners: The number of intimate partners the female has had, indicating their level of sexual activity. Protection Usage: A binary variable (0: Never, 1: Sometimes, 2: Always) representing the usage of protection during sexual activity. Symptoms: A binary variable (0: No symptoms, 1: Symptoms) indicating the presence or absence of symptoms related to STDs. Location: The location of the participant, categorized into general city/district areas, which can provide geographical context. Education: A binary variable (0: Low education, 1: High education) representing the education level of the participant. STD Testing History: A binary variable (0: No, 1: Yes) indicating whether the participant has a history of undergoing anonymous STD testing. STD Status: A binary variable (0: Uninfected, 1: Infected) reflecting the STD status of the female participants. Usage: This dataset serves as a valuable resource for researchers in the fields of healthcare, data security, and 3D modeling. Researchers can leverage this dataset to explore the relationship between demographic factors, behaviors, and STD prevalence among females. It is particularly relevant for studies that aim to enhance data security and privacy while utilizing 3D modeling techniques for healthcare applications. Data Privacy and Ethics: The collection of this dataset adheres to ethical and privacy considerations, with a focus on ensuring the anonymity and confidentiality of the study participants. Personal identifiers have been removed to protect the privacy of the individuals. Citation: If you intend to use this dataset in your research, please consider citing the source and acknowledging the data collection process. Proper citation helps maintain transparency and credit the researchers and institutions involved in data collection.



Machine Learning, Adolescent Sexually Transmitted Disease, Health Care