Economic Implications and Cost-Effectiveness of Implementing Machine Learning-Based Diabetes Prediction Programs

EasyChair Preprint 13592

Abstract

Machine learning-based diabetes prediction models have gained significant attention in healthcare as potential tools for early detection and management of diabetes. However, the successful implementation of these models relies heavily on the involvement of healthcare professionals. This abstract explores the role of healthcare professionals in implementing machine learning-based diabetes prediction models.

Healthcare professionals play a crucial role in the development and implementation of these models by collaborating with data scientists and machine learning experts. Their clinical expertise and domain knowledge contribute to identifying relevant data sources and variables for model development. They also ensure data quality and integrity, addressing ethical considerations throughout the process.

In the implementation phase, healthcare professionals are responsible for data collection and preprocessing, including gathering patient data from electronic health records and wearable devices. They ensure data privacy and security while cleaning and organizing the data for model input. Healthcare professionals evaluate and validate the model's performance and accuracy, assessing limitations and potential biases.

Keyphrases: Challenges, Data Quality, Diabetes, Prediction programs, data availability, implementation, machine learning

@booklet{EasyChair:13592,
  author    = {Ayuns Luz},
  title     = {Economic Implications and Cost-Effectiveness of Implementing Machine Learning-Based Diabetes Prediction Programs},
  howpublished = {EasyChair Preprint 13592},
  year      = {EasyChair, 2024}}