Diabetes mellitus is one of the major concerned diseases that cause a large number of deaths every year. It is considered as the chronic disease which is caused by an increase in blood sugar. If diabetes remains unidentified and untreated, it creates more complexities. So, the early prediction of diabetes can reduce the fatal rate of a human. The data mining concept assists to diagnose diabetes. Various research studies are presented with various data mining algorithms for early prediction and disease diagnosis but still with lack of accuracy. At the same time, mining the diabetes data in a secure manner is one of the critical issues. To recover this issue, this paper designs the new model for early prediction of diabetes with high accuracy. This research explores the enhanced principal component analysis for efficient feature extraction from the dataset. To achieve the highest accuracy of classification, it has proposed the machine learning algorithm, namely, modified support vector machine (MSVM) which is used to detect the diabetes disease at an early stage. The main contribution of this research is to mining the patient’s disease results in cloud security. For this security purpose, honey bee encryption and decryption algorithm is used. The performance measures of the proposed method are evaluated on various measures of accuracy, sensitivity, specificity, precision, and negative predictive value. Results obtained show the proposed MSVM classifier outperforms with the highest accuracy of 97.13%. We have compared the proposed methods with existing methods for proving our method has better performance.

糖尿病是每年造成大量死亡的主要关注疾病之一。它被认为是由血糖升高引起的慢性疾病。如果糖尿病仍未被识别和治疗，就会造成更多的复杂性。因此，糖尿病的早期预测可以降低​​人类的死亡率。数据挖掘概念有助于诊断糖尿病。各种研究提出了用于早期预测和疾病诊断的各种数据挖掘算法，但仍然缺乏准确性。同时，以安全的方式挖掘糖尿病数据是关键问题之一。为了解决这个问题，本文设计了一种高精度糖尿病早期预测新模型。本研究探索了增强的主成分分析，以便从数据集中有效地提取特征。为了达到最高的分类精度，提出了机器学习算法，即改进的支持向量机（MSVM），用于早期检测糖尿病疾病。这项研究的主要贡献是在云安全中挖掘患者的疾病结果。出于此安全目的，使用了蜜蜂加密和解密算法。所提出方法的性能指标是根据准确性、灵敏度、特异性、精确性和阴性预测值的各种指标进行评估的。获得的结果表明，所提出的 MSVM 分类器优于 97.13% 的最高精度。