Diabetes is the most common medical disorders that occur due to the malfunctioning of the pancreas. It increases the level of sugar in the body and poses a severe concern to human health by adversely affecting almost all major organs of the body, including kidney, heart, eyes, etc. The number of research works in the literature proves that machine learning techniques can increase the early detection of disease and decrease medical error rates to save human life. Developing an accurate and effective diabetes prediction model is always a challenge, as the medical dataset suffers from outliers and missing values. The aim of this study is to build an accurate and robust Diabetes Classification and Prediction Model (DCPM) on a dataset that suffers from the class imbalance problem and contains outliers and missing values. The proposed work devises an effective pre-processing technique to remove outliers, fill missing values, standardize data and select relevant features for model learning in a pipelined manner. The proposed pre-processing techniques were applied on the Pima Indian Diabetes (PID) dataset obtained from the University of California at Irvine (UCI) Repository. The K-NN classifier is optimized to find the optimum value of k and is then trained and evaluated on the most predictive set of features of the pre-processed PID dataset. The performance of the proposed model is assessed using classification accuracy, precision, recall and F1-score. The proposed approach is able to attain statistically good classification accuracy, recall, precision and F1-score as 92.28%, 92.36%, 92.38% and 92.31%, respectively. The proposed model outperforms existing state-of-the-art approaches in terms of accuracy. Therefore, the proposed DCPM can assist the medical experts by providing a quick, precise and reliable recommendation that can be considered while making a crucial decision about the health of a patient in the healthcare sector.

糖尿病是由于胰腺功能障碍而发生的最常见的医学疾病。它通过不利地影响人体的几乎所有主要器官，包括肾脏，心脏，眼睛等，增加了人体中的糖水平，并严重危害人体健康。文献研究的大量工作证明，机器学习技术可以增加疾病的早期发现并降低医疗错误率，以挽救生命。由于医疗数据集存在异常值和缺失值，因此开发准确有效的糖尿病预测模型始终是一项挑战。这项研究的目的是在遭受类不平衡问题并包含异常值和缺失值的数据集上建立准确而健壮的糖尿病分类和预测模型（DCPM）。拟议的工作设计了一种有效的预处理技术，以消除异常值，填充缺失值，标准化数据并选择相关特征以流水线方式进行模型学习。拟议的预处理技术应用于从加州大学尔湾分校（UCI）存储库获得的Pima印度糖尿病（PID）数据集。对K-NN分类器进行优化以找到k的最佳值，然后对预处理的PID数据集的最具预测性的特征集进行训练和评估。使用分类精度，精度，召回率和F1分数评估提出的模型的性能。所提出的方法能够在统计学上获得良好的分类准确性，召回率，准确性和F1分数，分别为92.28％，92.36％，92.38％和92.31％。在准确性方面，建议的模型优于现有的最新方法。因此，提出的DCPM可以通过提供快速，精确和可靠的建议来协助医学专家，在对医疗保健部门的患者的健康做出重要决定时可以考虑这些建议。