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A Decision Tree-Driven IoT systems for improved pre-natal diagnostic accuracy.

Xuewen Yang1, Ling Liu2, Yan Wang3

  • 1Prenatal Diagnosis Center, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China. yangxuewen@zzu.edu.cn.

BMC Medical Informatics and Decision Making
|December 5, 2024
PubMed
Summary

This study introduces an innovative prenatal diagnostic model using Internet of Things (IoT) devices and Machine Learning Decision Tree Algorithms. The integrated system enhances early detection of potential fetal health complications with 95% accuracy.

Keywords:
Decision tree algorithmHealth data analysisIoT technologyMaternal-fetal healthPre-natal diagnosticsReal-time monitoring

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Area of Science:

  • Medical Diagnostics
  • Health Informatics
  • Machine Learning in Healthcare

Background:

  • Prenatal diagnostics are crucial for maternal and fetal well-being.
  • Existing diagnostic methods face challenges in cost, accessibility, and timeliness.
  • There is a need for more effective and efficient prenatal screening tools.

Purpose of the Study:

  • To develop an integrated diagnostic model for prenatal care.
  • To leverage Internet of Things (IoT) innovation and Machine Learning (ML) for improved diagnostics.
  • To enhance the early identification and management of potential fetal health complications.

Main Methods:

  • Utilized IoT devices for real-time collection of vital maternal and fetal health data.
  • Implemented Decision Tree Algorithms (DTA) for analyzing large datasets of prenatal health records.
  • Trained and fine-tuned the DTA model using a comprehensive database of 1000 prenatal health records.

Main Results:

  • The proposed model achieved 95% accuracy in identifying potential health problems, surpassing classical statistical analysis (85%).
  • Demonstrated a 20% reduction in false positive cases and a 15% reduction in false negatives.
  • The system effectively flags abnormal data in real-time for healthcare professional attention.

Conclusions:

  • The integrated IoT and ML-based diagnostic model significantly improves prenatal screening accuracy and efficiency.
  • This approach offers a cost-effective and accessible solution for early detection of fetal health risks.
  • The enhanced diagnostic capabilities promise better maternal and fetal health outcomes.