Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2321814
Real-time data acquisition and analysis for predictive modelling of mental healthcare in Indian women with menstrual disorders: unveiling insights and implications from extensive surveys
M. Chengathir Selvi
; Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India
J. Chandra Priya
; Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India
*
M. Prasha Meena
; Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India
R. Jaya Swathika
; Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India
* Dopisni autor.
Sažetak
The consistency and duration of the menstrual cycle exhibit significant associations with specific
psychiatric conditions throughout an individual’s lifespan. The proposed methodology surveys
the relationship between psychiatric disorders and the length or regularity of the menstrual
cycle and analyzes the difficulties undergone by the women. A comprehensive dataset is generated and a mathematical model using an exploratory data analytics approach is developed, in
order to establish a correlation between these variables. It utilizes a cyclic methodology, leveraging shared menstrual data and a predictive model derived from vehicles to enhance network
learning. A decentralized secure learning procedure is implemented to ensure data privacy and
security. The transfer learning techniques helps to enhance the ability to learn from diverse
data distributions in IoMT (Internet of Medical Things) networks, improve the robustness of the
learning process. This approach presents a practical and effective solution for IoMT network
learning, allowing each participant to contribute their individual features to collectively extract
valuable insights from the data. The decentralization facilitates end-users in accessing their personal medical records while ensuring privacy, irrespective of their location and time. This system
also achieves a minimal delay sensitivity of 3.2%, by providing timely access to the required
information.
Ključne riječi
Menstrual cycle; mental disorders; IoMT; blockchain; Deep Learning
Hrčak ID:
326201
URI
Datum izdavanja:
28.2.2024.
Posjeta: 0 *