Medical experts indulge in numerous strategies for efficient and predictive measures to model the health status of patients and formulate the patterns that are formed in test results. Most patients would dream of their betterments of their health conditions and thus preventing the progression of any disease. When diabetics is considered in the model, or highly intervening methodology would be required for pre-diabetic individuals. Hidden Markov models have been modified into variant models to derive predictions that accurately produce expected results by investigating patterns of clinical observations from a detailed sample of patient’s dataset. There are yet unanswered and concerning challenges to derive an absolute model for predicting diabetes. The datasets from which the patterns are derived from, still holds levels of in completeness, irregularity and obvious clinical interventions during the diagnosis. The Electronic Medical Records are not furnished with all requisite information in all conditions and scenarios. Due to these irregularities prediction has become highly challenging and there is increase in misclassification rate. Newton’s Divide Difference Method (NDDM) is a conventional model for filling the irregularity in electronic datasets through divided differences. The classical approach considers a polynomial approximation approach, thus leading to Runge Phenomenon. If the interval between data fields id higher, severity of finding the irregularities is even higher. By using this type of technique it helps in improving the accuracy thereby bringing in high level prediction without any error and misclassification. In this technique proposed, a novel approximation technique is implemented using the Euclidean distance parameter over the NDDM approximation to predict the outcomes or risk of Type 2 Diabetes Mellitus among patients. Real world entities in CPCSSN are considered for this study and proposed method is tested. The proposed method filled the irregularity in the data components of EMR with better approximations and the quality of prediction has improved significantly.

医学专家沉迷于多种策略，以采取有效且具有预测性的措施来对患者的健康状况进行建模，并制定出测试结果中形成的模式。大多数患者会梦想着改善自己的健康状况，从而防止任何疾病的进展。当模型中考虑糖尿病患者时，或者糖尿病前个体需要高度干预的方法。隐藏的马尔可夫模型已被修改为变量模型，以通过从患者数据集的详细样本中调查临床观察的模式来得出准确产生预期结果的预测。尚无答案和令人担忧的挑战来推导用于预测糖尿病的绝对模型。模式所源自的数据集仍然保持完整性水平，诊断期间出现不规则和明显的临床干预措施。在所有情况和情况下，电子病历都未提供所有必需的信息。由于这些不规则性，预测变得非常具有挑战性，并且误分类率也在增加。牛顿的“分差法”（NDDM）是用于通过分差来填充电子数据集中的不规则性的常规模型。经典方法考虑了多项式逼近方法，因此导致了朗格现象。如果数据字段之间的间隔较大，则发现不规则性的严重性甚至更高。通过使用这种类型的技术，它有助于提高准确性，从而带来高级预测，而不会出现任何错误和错误分类。在这项技术中，在NDDM近似上使用欧几里德距离参数来实施一种新颖的近似技术，以预测患者中2型糖尿病的结局或风险。本研究考虑了CPCSSN中的现实世界实体，并对提出的方法进行了测试。所提出的方法用更好的近似值填充了EMR数据成分中的不规则性，并且预测质量得到了显着改善。