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Efficient than conventional methods

 Strategies to increase participation of community health professionals (CHWs) in the utilization of RMNCH–N could also help increase the frequency of utilization. The influence of CHWs on childcare and maternal use (except to help with the purpose of family planning) was found. In addition, research has revealed that women who have access to mass media are more likely to have higher likelihood of using RMNCH–N services. These findings point to the potential of mobile technology to increase women’s awareness and help CHW capacities.

 Future research areas for future research

 Machine learning techniques were more precise and efficient than conventional methods. They were better at capturing non-linear relationships, making this analysis more precise. While the algorithm for supervised learning employed in this knowingyourdebt knowing your debt Website knowing your debt com study was developed to minimize biases, it is not without limitations data and doesn’t provide an accurate view. To better comprehend the causal connections between access to financial services for low-income women and the importance of CHWs and the improvement in RMNCH–N results, more research is required.

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