Diabetes Dataset - 1161 Words

The dataset that we used is Pima.te. This dataset is from the National Institute of Diabetes and Digestive and Kidney Diseases. The objects of this dataset are the Indian females whose age are equal to or greater than 21, and they live near the Phoenix, Arizona. The reason that the participants were selected near the near Phoenix, AZ is because the incidence of diabetes is higher in this region since 1965. The purpose of this dataset is to analyze relativity of diabetes and other variables which including npreg (pregnant frequency), glu (blood glucose was tested by oral glucose), Bp (diastolic pressure), skin (the thickness of skin folds of the triceps), bmi (body max index), ped (diabetes ancestry function), and age. It consists 332†¦show more content†¦II. The histograms of the number of pregnancies, and age look right skewed †¢ In order to test the accuracy of the discovery from the histogram, we used QQ plot to compare each variable with normal distribution: Through the QQ plot, we can easily see that: I. Every attribute is normally distributed except diabetes pedigree function. II. The triceps skin fold thickness has highest normal distribution. †¢ In order to observe whether there exists a data point that particularly differs from other data, we use boxplot to analyze the outliers: Reviewing the boxplot of every attribute in the dataset indicates that: I. All of the attributes have outliers except plasma glucose concentration in an oral glucose tolerance test II. The diabetes pedigree function has most outliers among the all attributes. †¢ Measure the variability to see how the data spread out in the distribution by calculating the range, mean and standard deviation of each attributes. Upon computation, the plasma glucose concentration in an oral glucose tolerance test has biggest variance which means the data of this attribute is particularly scattered. †¢ In order to check if there are obvious linear relationships between each variable, we used the bivariate analysis: Observing the graph, we can easily get that: I. There is relatively high multicollinearity between triceps skin fold thickness and body mass index, age and number of pregnancies. II. The diabetic women giveShow MoreRelatedThe Dataset Diabetes Details From Efron Et Al1095 Words   |  5 PagesIndividual Project 1 Introduction This report will cover the dataset diabetes details from Efron et al.(2003). Throughout this report, we will explore the potential relationship between the ten predictor variables: age, sex, BMI, average blood pressure, and six blood serum measurements (tc, ldl, hdl, tch, ltg, glu) on a quantitative measure of disease progression one year after the baseline. There are 442 diabetes patients, or records in the dataset. In this report, we will explore different methods andRead MoreAn Accurate Prediction And Diagnostic Of A Disease Essay1050 Words   |  5 PagesAbstract—Diabetes is one of the leading causes of death, disability and economic loss throughout the world. Type 2 diabetes is more common (90-95% worldwide) type of diabetes. However, it can be prevented or delayed by taking the right care and interventions which indeed an early diagnosis. There has been much advancement in the field of various machine learning algorithms specifically for medical diagnosis. But due to partially complete medical data sets, accuracy often decreases, results in moreRead MoreDiabetes Mellitus is a Lifelong Metabolism Disorder1504 Words   |  6 PagesDiabetes Mellitus is a chronic, lifelong metabolism disorder that affects the ability of the body system to use the energy found in food. 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An important limitation of their study was that it did not directlyRead MoreDiabetes Is A Group Of Metabolic Diseases1505 Words   |  7 Pages Diabetes in African American Populations Melanie Barber, MSN Student MPH 855 Principles of Epidemiology Department of Baccalaureate Graduate Nursing, Eastern Kentucky University Richmond, KY November 21, 2016 According to the American Diabetes Association, diabetes mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. 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ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 1 A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE 1NASIB SINGH GILL, 2 POOJA MITTAL 1 Professor, Department of Computer Science Applications, Maharshi Dayanand University, Rohtak, Haryana, India 2 Assistant Professor, Department of Computer Science Applications, Maharshi Dayanand UniversityRead MoreThe Clustering Is A Data Mining Technique1173 Words   |  5 Pagesanalysis and mathematics. In this paper represents the performance of three clustering algorithms such as EM, DBSCAN and SimpleKMeans are evaluated. The Diabetes dataset is used for estimating and evaluating the time factor for predicting the performance of the algorithms by using clustering Techniques. Keywords: EM, DBSCAN, SimpleKMeans, Diabetes dataset. 1. Introduction: The clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each otherRead MoreImplementation Of The Machine Learning Classifier For Anomaly Detection1486 Words   |  6 Pagesimplementation of this project will be discussed. \section{Dataset selection} This was the most important part of the entire project and consumed a lot of time. Selecting a suitable database to perform the desired type of analysis was a very difficult task as there are a very few well organized and labeled medical databases which are suitable to perform anomaly detection. The database used by this project is the Pima Indians Diabetes database \cite{Dataset} which is a well structured and a labeled databaseRead MoreClassification and Dichotomy Case Study856 Words   |  3 Pagesfor the dataset previously are divided into two segments for training segment and testing segment. The number of training segment (t) and its features (trset) are given for the svmtrain MATLAB function. To evaluate the proposed work for the detection of diabetic retinopathy using MAs is performed by using the fundus images taken from publicly avalilable databases such as DRIVE [19], and DIARETDB1 [20] in MATLAB environment. The results obtained for each step is discussed here. The datasets specified