Abstract: Proactive and customized approaches are necessary when it comes to the medical care of expectant mothers and children. Even if early and accurate disease prediction is based on readily available symptom information, it can significantly improve outcomes by promoting timely therapies. Extensive testing and specialist visits are common components of traditional diagnosis techniques, which may be costly and time-consuming, especially in situations with limited resources. This study reconnoiters the potential of using Random Forest, a powerful machine learning algorithm, to predict diseases in children and pregnant women based on the symptoms that they exhibit. This offers a possible choice for improved healthcare delivery and early risk assessment. Making predictions about childhood diseases, including pneumonia, malaria, and malnutrition, based on reported symptoms, can significantly lower morbidity and death. A Random Forest model can identify the probability of certain diseases and provide rapid referrals for additional testing and treatment when symptoms like fever, cough, dyspnoea, and weight loss are entered. Communities that are geographically remote and have limited access to specialized medical care should pay special attention to this. The early diagnosis of conditions including gestational diabetes, preeclampsia, and anemia during pregnancy is crucial for the mother's and the unborn child's health. Early detection of the ailment allows for the timely implementation of preventative measures, such as changing one's lifestyle or taking medication. The accuracy of the proposed Child healthcare system is 92% which is greater than other present methods. This analysis is based on the information provided by parents about the symptoms of their child’s diseases.

Mahesh Ashok Mahant and P. Vidyullatha, “Framework for Child Healthcare System Using Random Forest” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160627

@article{Mahant2025,
title = {Framework for Child Healthcare System Using Random Forest},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160627},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160627},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Mahesh Ashok Mahant and P. Vidyullatha}
}