Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in the diagnosis of various diseases, including tropical fevers such as dengue and malaria. However, there is a lack of standard guidelines to develop the AI models, the gap between clinical and engineering expertise and clinical validation of the models, and hence there is a critical need for the development of an integrated diagnostic tool which uses demographical, laboratory variables and epidemiological parameters of patient and provides early prediction.

The present study aimed to develop and evaluate a machine-learning (ML) prediction tool for differential diagnosis of tropical fevers for adult patients (>18 years) using a three-phase approach in a tertiary care centre in South India by January 2026. Phase involves identification of the prevalent tropical fevers and associated clinical parameters to develop the AI model through a retrospective audit and qualitative interview. Phase Ⅱ involves retrospective data collection from hospital medical records for finalised diseases (1000 cases per disease) and clinical parameters, with data being used for model development using the Python language. Support vector machine, logistic regression, K-Nearest Neighbors, Naïve Bayes and ensemble models such as decision tree and Random Forest will be employed along with explainable AI techniques. They are used as they are easy to understand and interpret, well established, most effective for structured data, enhancing the transparency and interpretability of the predictive machine learning models, and their use has been widely supported in previous studies across various contexts. Suitable statistical parameters like specificity, sensitivity and area under receiver operating characteristic (AUROC) will be applied to evaluate model performance. In phase , the developed model will be implemented prospectively to assess the feasibility of model implementation. Model performance such as specificity, sensitivity and AUROC will be calculated, and the finally developed model will be implemented in a single tertiary care hospital to evaluate its overall performance.

Ethical approval for the study has been obtained from the institutional ethics committee of the Kasturba Medical College and Kasturba Hospital, Manipal (IEC number: 6/2024). Informed consent will be taken for obtaining the data of the patient for the evaluation of the model in the third phase of the study, and data will be kept confidential. The study results will be disseminated by publishing them in a peer-reviewed journal.

The protocol has been registered with the Clinical Trial Registry of India (CTRI) (CTRI/2024/04/065866) and approved on 16 April 2024.

The primary objective of this study is to investigate the perceived need and attitudinal perspectives regarding menstrual leave policies among young women in rural South India. The secondary objective was to determine the socio-demographic, menstrual and workplace-related factors associated with attitudes towards menstrual leave among young women.

An analytical cross-sectional study was performed from May 2023 to August 2023.

In a rural district of Tamil Nadu, South India.

The study encompassed 955 young female students above 18 years of age enrolled in educational institutions in a rural district of Tamil Nadu, India. Participants were pursuing diverse professional programmes including medical, dental, allied health sciences, pharmacy and engineering courses.

The primary outcomes included assessment of basic menstrual characteristics (age of menarche, regularity, product usage and pain experiences), pain evaluation using the WaLIDD scale (which measured working ability, anatomical pain location, pain intensity via Wong Baker scale and pain duration) and attitude assessment through a 10-dimension Likert scale. The attitude assessment explored both supportive factors (pain management, environmental considerations, medical leave allocation, menstruation normalisation and performance impact) and potential concerns (medicalisation, perceptions of fragility, stigma, disclosure issues and abnormal leave usage). Secondary outcome measures encompassed the analysis of factors influencing these attitudes, followed by a multivariable linear regression model to identify significant predictors.

Among 955 female students (mean age 19.56±1.33 years), the majority supported menstrual leave for maintaining hygiene (82.3%) and managing dysmenorrhoea (75.8%). A substantial proportion (64.4%) viewed it as a means of normalising menstruation discourse, while 61.6% believed it could enhance workplace performance. However, concerns existed about medicalising menstruation (47.9%) and reinforcing gender stereotypes (43.4%). Multivariate analysis revealed that medical students (B=0.67, 95% CI: 1.34 to 2.00), those with graduate-educated fathers (B=1.64, 95% CI: 0.31 to 2.97), earlier age at menarche (B=–0.23, 95% CI: –0.45 to –0.01) and participants reporting menstrual interference with daily activities (B=0.96, 95% CI: 0.02 to 0.89) held significantly more positive attitudes.

While young women generally support menstrual leave policies, particularly for hygiene and pain management, there are significant concerns about workplace stigmatisation and gender stereotyping. Educational background, parental education and personal menstrual experiences significantly influence attitudes toward menstrual leave. These findings suggest the need for carefully structured menstrual leave policies that balance biological needs with workplace/student place equality concerns.