## 2 Related Works  
Extensive research has been conducted on the use of machine learning and statistical methods to predict and prevent the occurrence of DM. This study highlights the urgent need to enhance early detection and intervention strategies for this common chronic disease. This section provides an overview of important contributions and methodologies in this field. It specifically examines the integration of different techniques for preparing data, machine learning algorithms, and the role of outlier detection using the MAH.  
MAH has been widely used for outlier detection in medical datasets owing to its ability to account for variable correlations. For instance, ref. [11] utilized the Mahalanobis distance to effectively detect and handle outliers, which improved the stability and performance of their hypertension prediction models. Ref. [22] challenges the Neural Network for medical images by applying Mahalanobis distance to detect any out-of-distribution (OOD) pattern using synthetic artifacts. The authors of [23] proposed a privacy-preservingdisease diagnosis scheme based on the Mahalanobis distance test, involving query users, aided cloud servers, and classification cloud servers. The system jointly calculates and protects sensitive medical data and outcomes from the clinical diagnostic processes. An innovative heart-sound-based system for diagnosing heart diseases was proposed using automatic adaptive feature extraction and the Mahalanobis-distance-classification criterion [24]. Similarly, various studies have employed outlier detection methods to predict diabetes. In diabetes prediction, removing outliers helps refine the dataset and enhances the performance of machine learning models.  
Recent advancements in diabetes prediction models have demonstrated the effectiveness of various machine learning algorithms, including LR, DT, CatBoost, XGBoost, RF, Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Neural Networks (NNs). Notably, a 2024 study using the SECNN model achieved 94.12% accuracy on the NHANES dataset and 89.47% accuracy on the PIMA Indian dataset [25]. Another study found that the CatBoost Classifier was the most accurate (85%) for predicting gestational diabetes [26]. Additionally, models such as the Deep Dense Layer Neural Network (DDLNN) [27] and RF [28] have been effectively applied to the PIMA Indian dataset. At the same time, [PERSON] et al. improved predictions through a weighted ensemble of models [29] yielding an accuracy of 95.83% in the RFWBP model [30]. Practical applications, such as [PERSON] et al.'s automated eHealth cloud system using an Extreme Learning Machine (ELM), have also shown promising results with an accuracy of 90% [31].  
Integrating outlier removal with machine learning models significantly advances diabetes prediction and prevention. By effectively identifying and handling outliers, these integrated approaches enhance the robustness and accuracy of predictive models. The continued exploration and development of such hybrid models hold great potential for improving diabetes management and ultimately reducing the burden of this chronic disease. The relevant studies are summarized in Table 1 and organized in chronological order.