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E-alive: An Integrated Platform Based on Machine Learning Techniques to Aware and Educate Common People with the Current Statistics of Maternal and Child Health Care

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Data Science and Big Data Analytics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 16))

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Abstract

Data science finds a variety of applications in day-to-day life. Its practical uses can cater to needs of improving the lifestyle and health standards of the individuals of the society. This paper proposes an intelligent tool, called E-alive, build to encourage people towards the sensitivity of maternal and child health care. This tool serves as an integrated platform for rural and urban people, government officials and policy makers to actively participate and analyse the current statistics of various parameters such as infant mortality rate, life expectancy ratios for females and males individually, female and male sterilization rates and maternal mortality rates for the next subsequent years. This can help them in taking quality decisions in order to improve upon the predicted values. Further this tool can assist in classifying the educational status of an individual, community and state on the basis of total fertility rates. This implies that the awareness factor among the people of respective community or state and total fertility rate can be predicted by this tool for the future years. The current analysis analyses the two government schemes in detail: Swadhar Scheme and Janani Suraksha Yojana. Other analysis factors include Life Expectancy Ratio, Education Details, Maternal Mortality Rate and the Contraceptive Methods used by people in major cities.

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Correspondence to Garima Malik .

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Malik, G., Vij, S., Tayal, D., Jain, A. (2019). E-alive: An Integrated Platform Based on Machine Learning Techniques to Aware and Educate Common People with the Current Statistics of Maternal and Child Health Care. In: Mishra, D., Yang, XS., Unal, A. (eds) Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-10-7641-1_3

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