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Medical crowdfunding in India during COVID-19: predictive modelling of campaign strength using XGBoost and random forest

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Listed:
  • Rangapriya Saivasan
  • Madhavi Lokhande

Abstract

Medical crowd funding emerged as an important channel, during COVID-19, to bridge the chasm between the overburdened public healthcare system and expensive private medical services. In India, the percentage of funds raised by such campaigns vis-à-vis the targeted amount is low in most cases. This study identifies the variables that have an impact on the performance of the campaign and leverages them to predict the 'campaign strength'. A sample of 305 COVID-related campaigns is analysed using descriptive statistics and text analytics. The strength of the campaigns is classified as - weak, moderate and strong based on their success rate (fundraised/target amount). Machine learning algorithms are used to predict the strength of the campaign. XGBoost predicts the campaign strength with 74.19% accuracy while Random Forest predicts campaign strength with 75% accuracy. The insights can be used to improve the layout design of the platforms, achieve better campaign strength and enhance stakeholders' experience.

Suggested Citation

  • Rangapriya Saivasan & Madhavi Lokhande, 2025. "Medical crowdfunding in India during COVID-19: predictive modelling of campaign strength using XGBoost and random forest," International Journal of Business Innovation and Research, Inderscience Enterprises Ltd, vol. 38(4), pages 505-529.
    • Handle: RePEc:ids:ijbire:v:38:y:2025:i:4:p:505-529
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