This paper is in the following e-collection/theme issue:

Non-randomized Protocols and Methods (ehealth) (575)

Published on in Vol 9, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17619, first published .
Using Machine Learning to Optimize the Quality of Survey Data: Protocol for a Use Case in India

Using Machine Learning to Optimize the Quality of Survey Data: Protocol for a Use Case in India

Using Machine Learning to Optimize the Quality of Survey Data: Protocol for a Use Case in India

Authors of this article:

Neha Shah1 Author Orcid Image ;   Diwakar Mohan1 Author Orcid Image ;   Jean Juste Harisson Bashingwa2 Author Orcid Image ;   Osama Ummer3 Author Orcid Image ;   Arpita Chakraborty3 Author Orcid Image ;   Amnesty E. LeFevre1, 4 Author Orcid Image
Article Authors Cited by (7) Tweetations (17) Metrics

Journals

  1. Fletcher R, Nakeshimana A, Olubeko O. Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health. Frontiers in Artificial Intelligence 2021;3 View
  2. Shah N, Ummer O, Scott K, Bashingwa J, Penugonda N, Chakraborty A, Sahore A, Mohan D, LeFevre A. SMS feedback system as a quality assurance mechanism: experience from a household survey in rural India. BMJ Global Health 2021;6(Suppl 5):e005287 View
  3. Dadhich A, Dadhich P, Goyal R. Synthesis of water, sanitation, and hygiene (WaSH) spatial pattern in rural India: an integrated interpretation of WaSH practices. Environmental Science and Pollution Research 2022;29(57):86873 View
  4. Saha S, Narayanan S. A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI). World Development 2022;154:105860 View
  5. Rao M, Sahu D, Nair S, Sharma R, Gulati B, Acharya R, Mahapatra B, Ramesh S, Khan N, Chaudhuri T, Sandal K, Deepani V, Dey S, Saggurti N. National guidelines for data quality in surveys. Indian Journal of Medical Research 2022;156(6):715 View
  6. Chapman A. Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes. Energies 2023;16(13):4911 View
  7. Kularathne S, Perera A, Rathnayake N, Rathnayake U, Hoshino Y, Mohamad Taghvaee V. Analyzing the impact of socioeconomic indicators on gender inequality in Sri Lanka: A machine learning-based approach. PLOS ONE 2024;19(12):e0312395 View