Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity in the present scenario. In developing country like India, early detection and prevention will be more cost effective. Oral Glucose Tolerance Test (OGTT) is the crucial method for diagnosing GDM done usually between 24th and 28th week of pregnancy. The proposed work focuses on early detection of GDM without a visit to the hospital for women who are pregnant for the second time onwards (multigravida patients). A decision support system using Multilayer Neural Network which learns to classify GDM and non GDM patients using Back 3328 Priya Shirley Muller et al. Propagation learning algorithm is developed. The classifier proves to be an efficient model for diagnosis of GDM without the conventional method of blood test by providing newly designed parameters as inputs to the network.

The prevalence of both obesity and Gestational Diabetes Mellitus (GDM) is increasing worldwide. Overweight and obesity are abnormal or excessive fat accumulation that presents a risk to health. The presence of obesity has, in particular, a significant impact on both maternal and fetal complications associated with GDM. These complications can be addressed, at least in part, by good glycaemic control during pregnancy. The objective of the study is to classify GDM and non-GDM patients based on pre-pregnancy maternal Body Mass Index (BMI) and to assess and quantify the risk for GDM according to BMI.

Weather can be defined as the condition of air on earth for a given time and at a given place. Weather prediction has been of great interest and a challenging task for researchers for so many years. To predict weather so many factors have to be considered like temperature, atmospheric pressure, humidity, wind pressure etc. In particular temperature prediction plays a vital role for planning in so many fields like agriculture, industry, climatic conditions and so on. Various statistical and computational methods are applied for temperature prediction so far and literature survey shows that machine learning methods outperformed the standard traditional methods. In this paper, one of the machine learning algorithm K-Nearest Neighbour algorithm (KNN) is applied for predicting annual mean temperature of India based on the annual maximum and annual minimum temperatures of India. The proposed model predicts annual mean temperature and the findings are compared using the error measure MAPE.

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