Authors: Duru Shah, Vishesha Yadav, Uday Pratap Singh, Abhik Sinha, Neha Dumka, Rupsa Banerjee, Rashmi Shah, Jyoti Unni, Venugopala Rao Manneni
This study aimed to identify peri- and post-menopausal women at risk of non-communicable diseases in rural India using artificial intelligence. Conducted in collaboration with the Government of Maharashtra, the observational study focused on women aged 45-60 from three villages in Latur district. Accredited social health activist workers identified 400 women, of whom 316 were analyzed. The study found high prevalence rates of dyslipidaemia (58%), osteopenia (50%), diabetes (25%), obesity (25%), and hypertension (20%). Although no direct correlation was found between symptoms and these diseases, predictive network analysis charts using clusters of symptoms suggested the presence of hypertension, diabetes, osteoporosis, and hypothyroidism. The findings highlight the potential of AI-based screening tools for early diagnosis and timely treatment of non-communicable diseases with the help of community health workers.
Link: Prevalence of non-communicable chronic diseases in rural India amongst peri- and post-menopausal women: Can artificial intelligence help in early identification?
Resource Topic: Chronic conditions, NON Communicable Diseases/NCD
Resource Type: Observational study
Year: 2024
Region: Asia
Country: India
Publisher May Restrict Access: Yes