Challenges and Limitations in Harnessing Machine Learning for Early Diabetes Prediction

EasyChair Preprint 13786

Abstract

The application of machine learning in early diabetes prediction holds great promise for improving healthcare outcomes. However, several challenges and limitations hinder the effective harnessing of machine learning for this purpose. This abstract provides an overview of the key challenges and limitations in utilizing machine learning for early diabetes prediction.

One significant challenge is the availability and quality of data. Insufficient and limited data, along with the lack of standardized data collection methods, pose obstacles in training accurate prediction models. Moreover, data quality issues, such as missing values and outliers, can impact the reliability of the predictions. Addressing these challenges requires efforts in data acquisition, standardization, and ensuring data integrity.

Another challenge lies in feature selection and extraction. Identifying relevant features from complex and high-dimensional data is crucial for accurate predictions. Integrating multiple data sources effectively and extracting meaningful features from raw data present additional complexities in the modeling process.

Interpretability and explainability are critical factors in healthcare applications. However, machine learning models often operate as black boxes, making it difficult to understand and interpret their decisions. Achieving a balance between model performance and interpretability is essential for gaining trust and acceptance among healthcare professionals and patients.

Generalization and external validation of prediction models also pose challenges. Models trained on specific patient populations may not generalize well to diverse populations or different healthcare systems. Lack of external validation on independent datasets and the risk of overfitting in real-world scenarios further limit the reliability and applicability of the models.

Keyphrases: Adoption, Clinical integration, Early Diabetes, Workflows, healthcare professionals, healthcare settings, prediction models

@booklet{EasyChair:13786,
  author    = {Kayode Sheriffdeen and Samon Daniel},
  title     = {Challenges and Limitations in Harnessing Machine Learning for Early Diabetes Prediction},
  howpublished = {EasyChair Preprint 13786},
  year      = {EasyChair, 2024}}