Role of Feature Engineering in Improving Machine Learning Predictions of Diabetes Mellitus in Healthcare Data

Diabetes Mellitus is a global health concern, and early prediction plays a crucial role in its management and prevention. In the realm of healthcare, machine learning (ML) has shown promising potential for predicting the onset and progression of diabetes. However, the effectiveness of ML models heavily relies on the quality of the data fed into them. Feature engineering—the process of transforming raw data into informative features—emerges as a critical factor in improving prediction accuracy and model performance. This paper explores the role of feature engineering in enhancing ML-based predictions of Diabetes Mellitus using healthcare data. Key techniques such as data preprocessing, feature selection, transformation, and dimensionality reduction are examined for their impact on model outcomes. By refining the features extracted from clinical data (e.g., glucose levels, BMI, age, and medical history), these methods can improve predictive accuracy and reduce model overfitting. Additionally, this paper discusses challenges such as data imbalance, missing values, and ethical concerns surrounding the use of patient data. The paper also outlines future directions, including the integration of automated feature engineering and the potential for personalized diabetes prediction models. The findings underscore the transformative potential of feature engineering in healthcare, highlighting its ability to drive more accurate, reliable, and actionable diabetes predictions.