Authors - Hemal S, Sohana R, M Shahina Parveen, Tarun Pradeep Kumar Abstract - Childhood fever poses a significant health concern in India, necessitating timely intervention and effective healthcare strategies. However, predicting fever prevalence accurately remains a challenge due to the diverse healthcare landscape and maternal-child health indicators. This research aims to develop a systematic methodology for predicting childhood fever prevalence based on maternal and child healthcare indicators in India. Leveraging machine learning algorithms, particularly Support Vector Regression (SVR), the study seeks to provide an effective tool for early detection and intervention in infant fever cases. Using data from the "India - Annual Health Survey (AHS) 2012-13" dataset, specific maternal and child healthcare indicators relevant to childhood fever prevalence are identified. These indicators encompass ante-natal care, delivery care, immunization, breastfeeding, and supplementation practices. Various regression algorithms, including SVR, are trained and evaluated to accurately predict childhood fever prevalence. Experimental results demonstrate that SVR outperforms other regression algorithms, showcasing its effectiveness in capturing non-linear relationships and handling outliers. This study offers a structured framework for early detection and intervention in childhood fever cases, leveraging machine learning algorithms and maternal-child health indicators. By accurately predicting fever prevalence, healthcare practitioners can implement timely interventions, ultimately improving healthcare outcomes for infants in India.
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