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Pima Indian Diabetes Prediction

Glucose Regulation And Prediction Of Cardiovascular Disease And Mortality In Pima Indians

Glucose Regulation And Prediction Of Cardiovascular Disease And Mortality In Pima Indians

Glucose Regulation and Prediction of Cardiovascular Disease and Mortality in Pima Indians Impaired glucose regulation (i Impaired glucose regulation (impaired glucose tolerance (IGT) and/or impaired fasting glucose (IFG)) predicts all-cause and cardiovascular disease (CVD) mortality in some, but not all populations. We computed incidence rates of major ischemic ECG changes, based on Minnesota codes, in 1,878 Pima Indians and death rates from natural causes and from CVD in 2,994 Pima Indians according to five glucose regulation categories defined by current ADA criteria (normal glucose regulation (NGR), IGT alone, IFG alone, combined IGT and IFG (Combined), and diabetes (DM)). During a median follow-up of 7.67 years (range 1.03-27.6 years), 303 subjects developed major ischemic ECG changes; similarly 780 individuals died (229 from CVD) during a median follow up of 10.4 years (range 0.04-29.0 years). The age- sex-adjusted incidence of major ischemic ECG changes was higher, relative to NGR, in those with diabetes duration of [ge]10 years. Likewise, death rates from natural causes or from CVD were similar within the nondiabetic groups, increasing only after the onset of diabetes and in association with diabetes duration (Table). After additional adjustment for body mass index, serum cholesterol, blood pressure, and smoking in a Cox proportional-hazards model, only subjects with [ge]15 years of diabetes had higher rates of major ischemic ECG changes (hazard rate ratio (HRR)=2.05; 95% CI=1.25-3.35, relative to NGR) and subjects with diabetes duration of [ge] 5 years had increased CVD mortality (HRR=3.14, 95% CI=1.85-5.34; HRR=3.92, 95% CI=2.36-6.53 and HRR=5.75, 95% CI=3.23-10.07 for DM 5-10 years, DM 10-15 years and DM [ge]15 years respectively, relative to NGR). The rates Continue reading >>

Diabetes Prediction With Deep Learning Studio: A Different Approach Towards Deeplearning

Diabetes Prediction With Deep Learning Studio: A Different Approach Towards Deeplearning

Torture the data, and it will confess to anything. My professional portfolio website:- Diabetes Prediction with Deep Learning Studio: A Different approach towards DeepLearning Diabetes is a disease that occurs when your blood glucose, also called blood sugar, is too high. Blood glucose is your main source of energy and comes from the food you eat. Insulin, a hormone made by the pancreas, helps glucose from food get into your cells to be used for energy. Sometimes your body doesn't make enough or any insulin or doesn't use insulin well. Glucose then stays in your blood and doesn't reach your cells. Over time, having too much glucose in your blood can cause health problems. Although diabetes has no cure, so it is important to detect whether you have diabetes or not. In this article I will build a simple neural network to categorize given input into two classes, one that contains diabetes and other that doesn't, using Deep Learning Studio If you are not familiar with how to use Deep Learning Studio take a look at this:) Is Deep Learning without Programming Possible?medium.com This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test BloodPressure: Diastolic blood pressure (mm Hg) SkinThickness: Triceps skin fold thickness (mm) BMI: Body mass index (weight in kg/(height in m)) Diabetes Pedigree Function: Diabetes pedigree function Age: Age (years) Information: The Pima Indians Continue reading >>

Machine Learning: Pima Indians Diabetes

Machine Learning: Pima Indians Diabetes

Sat 14 April 2018| in Development | tags: Machine Learning Python scikit-learn tutorial The Pima are a group of Native Americans living in Arizona. A genetic predisposition allowed this group to survive normally to a diet poor of carbohydrates for years. In the recent years, because of a sudden shift from traditional agricultural crops to processed foods, together with a decline in physical activity, made them develop the highest prevalence of type 2 diabetes and for this reason they have been subject of many studies. The dataset includes data from 768 women with 8 characteristics, in particular: Plasma glucose concentration a 2 hours in an oral glucose tolerance test Body mass index (weight in kg/(height in m)^2) The last column of the dataset indicates if the person has been diagnosed with diabetes (1) or not (0) The original dataset is available at UCI Machine Learning Repository and can be downloaded from this address: The type of dataset and problem is a classic supervised binary classification. Given a number of elements all with certain characteristics (features), we want to build a machine learning model to identify people affected by type 2 diabetes. To solve the problem we will have to analyse the data, do any required transformation and normalisation, apply a machine learning algorithm, train a model, check the performance of the trained model and iterate with other algorithms until we find the most performant for our type of dataset. # We import the libraries needed to read the datasetimport osimport pandas as pdimport numpy as np # We placed the dataset under datasets/ sub folderDATASET_PATH = 'datasets/' # We read the data from the CSV filedata_path = os.path.join(DATASET_PATH, 'pima-indians-diabetes.csv')dataset = pd.read_csv(data_path, header=None)# Bec Continue reading >>

Machine Learning Workflow On Diabetes Data: Part01

Machine Learning Workflow On Diabetes Data: Part01

Machine Learning Workflow on Diabetes Data: Part01 Image credit Machine learning in a medical setting can help enhance medical diagnosis dramatically. This article will portray how data related to diabetes can be leveraged to predict if a person has diabetes or not. More specifically, this article will focus on how machine learning can be utilized to predict diseases such as diabetes. By the end of this article series you will be able to understand concepts like data exploration, data cleansing, feature selection, model selection, model evaluation and apply them in a practical way. Diabetes is a disease which occurs when the blood glucose level becomes high, which ultimately leads to other health problems such as heart diseases, kidney disease etc. Diabetes is caused mainly due to the consumption of highly processed food, bad consumption habits etc. According to WHO , the number of people with diabetes has been increased over the years. Anaconda (Scikit Learn, Numpy, Pandas, Matplotlib, Seaborn) Basic understanding of supervised machine learning methods: specifically classification. As a Data Scientist the most tedious task which we encounter is the acquiring and the preparation of a data set. Even though there is an abundance of data in this era, it is still hard to find a suitable data set which suits the problem you are trying to tackle. If there arent any suitable data sets to be found, you might have to create your own. In this tutorial we arent going to create our own data set, instead we will be using an existing data set called the Pima Indians Diabetes Database provided by the UCI Machine Learning Repository (famous repository for machine learning data sets). We will be performing the machine learning workflow with the Diabetes Data set provided above. When en Continue reading >>

Github - Niharikagulati/diabetesprediction: Using Pima Indians Diabetes Data Set To Predict Whether A Patient Has Diabetes Or Not Based Upon Patients Lab Test Result Variables Like Glucose, Blood Pressure, Etc. Using Cart Decision Tree Algorithm And K-nearest Model Achieving 76% Accuracy. Python-scikit Learn, Scipy, Pandas, Matplotlib.

Github - Niharikagulati/diabetesprediction: Using Pima Indians Diabetes Data Set To Predict Whether A Patient Has Diabetes Or Not Based Upon Patients Lab Test Result Variables Like Glucose, Blood Pressure, Etc. Using Cart Decision Tree Algorithm And K-nearest Model Achieving 76% Accuracy. Python-scikit Learn, Scipy, Pandas, Matplotlib.

Dataset Link: From the domain knowledge, I have analyzed and found out the ranges of values and its effects on diabetes for each continuous variable in the dataset. Based upon these ranges we will categorize the continuous variables for implementing the decision tree in the next step. Also, we can utilize these ranges to come up with appropriate null value replacement for each independent variable.There are 8 independent variables: Glucose: Plasma Glucose Concentration a 2 hour in an oral glucose tolerance test (mg/dl)A 2-hour value between 140 and 200 mg/dL (7.8 and 11.1 mmol/L) is called impaired glucose tolerance. This is called "pre- diabetes." It means you are at increased risk of developing diabetes over time. A glucose level of 200 mg/dL (11.1 mmol/L) or higher is used to diagnose diabetes. Blood Pressure: Diastolic Blood Pressure(mmHg):If Diastolic B.P > 90 means High B.P (High Probability of Diabetes)Diastolic B.P < 60 means low B.P (Less Probability of Diabetes) Skin Thickness: Triceps Skin Fold Thickness (mm) A value used to estimate body fat. Normal Triceps SkinFold Thickness in women is 23mm. Higher thickness leads to obesity and chances of diabetes increases. Insulin: 2-Hour Serum Insulin (mu U/ml)Normal Insulin Level 16-166 mIU/LValues above this range can be alarming. BMI: Body Mass Index (weight in kg/ height in m2)Body Mass Index of 18.5 to 25 is within the normal rangeBMI between 25 and 30 then it falls within the overweight range. A BMI of 30 or over falls within the obese range. Diabetes Pedigree Function: It provides information about diabetes history in relatives and genetic relationship of those relatives with patients. Higher Pedigree Function means patient is more likely to have diabetes. Outcome: Class Variable (0 or 1) where 0 denotes patien Continue reading >>

Familiality Of Physical And Metabolic Characteristics That Predict The Development Of Non-insulin-dependent Diabetes Mellitus In Pima Indians.

Familiality Of Physical And Metabolic Characteristics That Predict The Development Of Non-insulin-dependent Diabetes Mellitus In Pima Indians.

Familiality of physical and metabolic characteristics that predict the development of non-insulin-dependent diabetes mellitus in Pima Indians. This article has been cited by other articles in PMC. Susceptibility to non-insulin-dependent diabetes mellitus (NIDDM) is largely genetically determined. In Pima Indians, obesity, insulin resistance, and a low acute insulin response (AIR) to an intravenous glucose infusion are each predictors of the disease. To ascertain whether these phenotypes are genetically determined, we estimated their familiality in nondiabetic Pima Indians with a maximum-likelihood method. Percentage body fat (PFAT) was highly familial (h2 =.76), whereas waist/ thigh circumference ratio (W/T ratio) was not significantly familial after controlling for PFAT (h2 = .16). AIR was also highly familial (h2 = .80 at 10 min), even after controlling for PFAT and insulin action (h2 = .70). Insulin action at physiologic plasma insulin concentrations was familial (h2 = .61) but less so after controlling for PFAT and W/T ratio (h2 = .38). At maximally stimulating insulin concentrations, insulin action was familial (h2 = .45) and was less influenced by controlling for PFAT and W/T ratio (h2 = .49). We conclude that in Pima Indians (1) PFAT and AIR are highly familial traits, (2) central distribution of fat is not a familial trait when controlled for PFAT, (3) 38%-49% of the variance in insulin action, independent of the effect of obesity, is familial, and (4) PFAT, AIR, and insulin action are useful traits to study genetic susceptibility to NIDDM. Because genetic parameter estimates are applicable only to the populations from which they were estimated, it is important to determine whether these estimates of familialities in Pima Indians can be confirmed in other popul Continue reading >>

An Efficient Prediction Model For Diabetic Database Using Soft Computing Techniques

An Efficient Prediction Model For Diabetic Database Using Soft Computing Techniques

An Efficient Prediction Model for Diabetic Database Using Soft Computing Techniques Part of the Lecture Notes in Computer Science book series (LNCS, volume 5908) Organizations aim at harnessing predictive insights, using the vast real-time data stores that they have accumulated through the years, using data mining techniques. Health sector, has an extremely large source of digital data - patient-health related data-store, which can be effectively used for predictive analytics. This data, may consists of missing, incorrect and sometimes incomplete values sets that can have a detrimental effect on the decisions that are outcomes of data analytics. Using the PIMA Indians Diabetes dataset, we have proposed an efficient imputation method using a hybrid combination of CART and Genetic Algorithm, as a preprocessing step. The classical neural network model is used for prediction, on the preprocessed dataset. The accuracy achieved by the proposed model far exceeds the existing models, mainly because of the soft computing preprocessing adopted. This approach is simple, easy to understand and implement and practical in its approach. Imputation MethodData Mining TechniquePima Indian DiabetesIncomplete Information SystemNeural Network Prediction Model These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access Unable to display preview. Download preview PDF. Mitra, S., Acharya, T.: Data Mining, Multimedia, Soft-computing and Bioinformatics. Wiley Interscience, Hoboken (2004) Google Scholar Little, R.J.A., Rubin, D.B.: Statistical Analysis with Missing Data. John Wiley, New York (1987) MATH Google Scholar Zhang, S., Qin, Continue reading >>

Github - Kriaga/pima-indians-diabetes-dataset-classification: Predicting If A Patient Is Suffering From Diabetes Or Not Using Machine Learning In Python

Github - Kriaga/pima-indians-diabetes-dataset-classification: Predicting If A Patient Is Suffering From Diabetes Or Not Using Machine Learning In Python

Predicting if a patient is suffering from Diabetes or not using Machine Learning in Python If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Pima Indians Diabetes Dataset Classification AbstractThe diabetes dataset is a binary classification problem where it needs to be analysed whether a patient is suffering from the disease or not on the basis of many available features in the dataset. Different methods and procedures of cleaning the data, feature extraction, feature engineering and algorithms to predict the onset of diabetes are used based for diagnostic measure on Pima Indians Diabetes Dataset. Keywordsmachine learning; Pima Indians Diabetes dataset; binary classification; features; feature extraction; feature engineering; support vector machine; MLP; neural netwroks; Decision tree; Linear regression heat map; pairplot; violin plot; feature importance. Database - Pima Indians Diabetes DatasetPima Indian Diabetes dataset has 9 attributes in total. All the person in records are females and the number of pregnancies they have had has been recorded as the first attribute of the dataset. Second is the value of Plasma glucose concentration a 2 hours in an oral glucose tolerance test and then is the Diastolic blood pressure (mm Hg), fourth in line is the Triceps skin fold thickness (mm), then is the 2-Hour serum insulin (mu U/ml), sixth is Body mass index (weight in kg/ (height in m) ^2) and then seventh is the Diabetes pedigree function and the second last value is the that of the Age (years). The ninth column is that of the Class variable (0 or 1), 0 for no diabetes and 1 Continue reading >>

R: Diabetes In Pima Indian Women

R: Diabetes In Pima Indian Women

A population of women who were at least 21 years old, of Pima Indian heritageand living near Phoenix, Arizona, was tested for diabetesaccording to World Health Organization criteria. The datawere collected by the US National Institute of Diabetes and Digestive andKidney Diseases. We used the 532 complete records after dropping the(mainly missing) data on serum insulin. These data frames contains the following columns: plasma glucose concentration in an oral glucose tolerance test. body mass index (weight in kg/(height in m)\^2). Yes or No, for diabetic according to WHO criteria. The training set Pima.tr contains a randomly selected set of 200subjects, and Pima.te contains the remaining 332 subjects.Pima.tr2 contains Pima.tr plus 100 subjects withmissing values in the explanatory variables. Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C.and Johannes, R. S. (1988)Using the ADAP learning algorithm to forecast the onset ofdiabetes mellitus.In Proceedings of the Symposium on Computer Applications inMedical Care (Washington, 1988), ed. R. A. Greenes,pp. 261265. Los Alamitos, CA: IEEE Computer Society Press. Ripley, B.D. (1996)Pattern Recognition and Neural Networks.Cambridge: Cambridge University Press. Continue reading >>

Type 2 Diabetes Mellitus Prediction Model Based On Data Mining

Type 2 Diabetes Mellitus Prediction Model Based On Data Mining

Type 2 diabetes mellitus prediction model based on data mining Author links open overlay panel HanWu ShengqiYang Due to its continuously increasing occurrence, more and more families are influenced by diabetes mellitus. Most diabetics know little about their health quality or the risk factors they face prior to diagnosis. In this study, we have proposed a novel model based on data mining techniques for predicting type 2 diabetes mellitus (T2DM). The main problems that we are trying to solve are to improve the accuracy of the prediction model, and to make the model adaptive to more than one dataset. Based on a series of preprocessing procedures, the model is comprised of two parts, the improved K-means algorithm and the logistic regression algorithm. The Pima Indians Diabetes Dataset and the Waikato Environment for Knowledge Analysis toolkit were utilized to compare our results with the results from other researchers. The conclusion shows that the model attained a 3.04% higher accuracy of prediction than those of other researchers. Moreover, our model ensures that the dataset quality is sufficient. To further evaluate the performance of our model, we applied it to two other diabetes datasets. Both experiments' results show good performance. As a result, the model is shown to be useful for the realistic health management of diabetes. Continue reading >>

End-to-end Example: Using Logistic Regression For Predicting Diabetes | Commonlounge

End-to-end Example: Using Logistic Regression For Predicting Diabetes | Commonlounge

We have our data saved in a CSV file called diabetes.csv. We first read our dataset in a pandas dataframe called diabetesDF, and then use the head() function to show the first five records from our dataset. First 5 records in the Pima Indians Diabetes Database The following features have been provided to help us predict whether a person is diabetic or not: Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test BloodPressure: Diastolic blood pressure (mm Hg) SkinThickness: Triceps skin fold thickness (mm) BMI: Body mass index (weight in kg/(height in m)2) DiabetesPedigreeFunction: Diabetes pedigree function (a function which scores likelihood of diabetes based on family history) Outcome: Class variable (0 if non-diabetic, 1 if diabetic) Let's also make sure that our data is clean (has no null values, etc). Note that the data does have some missing values (see Insulin = 0) in the samples in the previous figure. For the model we will be using, (logistic regression), values of 0 automatically imply that the model will simply be ignoring these values. Ideally we could replace these 0 values with the mean value for that feature, but we'll skip that for now. Let us now explore our data set to get a feel of what it looks like and get some insights about it. Let's start by finding correlation of every pair of features (and the outcome variable), and visualize the correlations using a heatmap. Output of feature (and outcome) correlations Heatmap of feature (and outcome) correlations In the above heatmap, brighter colors indicate more correlation. As we can see from the table and the heatmap, glucose levels, age, BMI and number of pregnancies all have significant correlation with the outcome variable. Also notice the correlation between pairs of feat Continue reading >>

Case Study: Predicting The Onset Of Diabetes Within Five Years (part 1 Of 3)

Case Study: Predicting The Onset Of Diabetes Within Five Years (part 1 Of 3)

Case Study: Predicting the Onset of Diabetes Within Five Years (part 1 of 3) This is a guest post by Igor Shvartser,a clever young student I have been coaching. This post is part 1 in a 3 part series on modeling the famous Pima Indians Diabetes dataset that will introduce the problem and the data. Part 2 will investigate feature selection and spot checking algorithms and Part 3 in the series will investigate improvements to the classification accuracy and final presentation of results. Need more help with Weka for Machine Learning? Take my free 14-day email course and discover how to use the platform step-by-step. Click to sign-up and also get a free PDF Ebook version of the course. Data mining and machine learning is helping medical professionals make diagnosis easier by bridging the gap between huge data sets and human knowledge. We can begin to apply machine learning techniques for classification in a dataset that describes a population that is under a high risk of the onset of diabetes. Diabetes Mellitus affects 382 million people in the world, and the number of people with type-2 diabetes is increasing in every country. Untreated, diabetes can cause many complications . The population for this study was the Pima Indian population near Phoenix, Arizona. The population has been under continuous study since 1965 by the National Institute of Diabetes and Digestive and Kidney Diseases because of its high incidence rate of diabetes. For the purposes of this dataset, diabetes was diagnosed according to World Health Organization Criteria, which stated that if the 2 hour post-load glucose was at least 200 mg/dl at any survey exam or if the Indian Health Service Hospital serving the community found a glucose concentration of at least 200 mg/dl during the course of routine m Continue reading >>

Using A Neural Network To Predict Diabetes In Pima Indians

Using A Neural Network To Predict Diabetes In Pima Indians

Using a neural network to predict diabetes in Pima indians Created an 95% accurate neural network to predict the onset of diabetes in Pima indians. Pretty cool! #theano. Needed to navigate to c:/users/Alex Ko/.keras/keras.json and change tensorflow to theano#Create first network with Kerasimport kerasfrom keras.models import Sequentialfrom keras.layers import Denseimport numpyimport pandas as pdimport sklearnfrom sklearn.preprocessing import StandardScaler# fix random seed for reproducibilityseed = 7numpy.random.seed(seed)# load pima indians datasetdataset = numpy.loadtxt('pima-indians-diabetes.csv', delimiter=",")#dataset = pd.read_csv('pima-indians-diabetes.csv')data=pd.DataFrame(dataset) #data is panda but dataset is something elseprint(data.head())# split into input (X ie dependent variables) and output (Y ie independent variables) variablesX = dataset[:,0:8] #0-8 columns are dependent variables - remember 8th column is not includedY = dataset[:,8] #8 column is independent variable# = StandardScaler()X = scaler.fit_transform(X)# create modelmodel = Sequential()# model.add(Dense(1000, input_dim=8, init='uniform', activation='relu')) # 1000 neurons# model.add(Dense(100, init='uniform', activation='tanh')) # 100 neurons with tanh activation functionmodel.add(Dense(500, init='uniform', activation='relu')) # 500 neurons# 95.41% accuracy with 500 neurons# 86.99% accuracy with 100 neurons# 85.2% accuracy with 50 neurons# 81.38% accuracy with 10 neuronsmodel.add(Dense(1, init='uniform', activation='sigmoid')) # 1 output neuron# Compile modelmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])# Fit the modelmodel.fit(X, Y, nb_epoch=150, batch_size=10, verbose=2) # 150 epoch, 10 batch size, verbose = 2# evaluate the modelscores = model.evaluate(X Continue reading >>

Understanding K-nearest Neighbours With The Pima Indians Diabetes Dataset

Understanding K-nearest Neighbours With The Pima Indians Diabetes Dataset

Understanding k-Nearest Neighbours with the PIMA Indians Diabetes dataset K nearest neighbors (kNN) is one of the simplest supervised learning strategies: given a new, unknown observation, it simply looks up in the reference database which ones have the closest features and assigns the predominant class. Let's try and understand kNN with examples. #Importing required packagesfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn import metricsfrom sklearn.cross_validation import train_test_splitimport matplotlib.pyplot as pltimport matplotlib as mplimport numpy as npimport seabornfrom pprint import pprint%matplotlib inline #Let's begin by exploring one of scikit-learn's easiest sample datasets, the Iris.from sklearn.datasets import load_irisiris = load_iris()print iris.keys() ['target_names', 'data', 'target', 'DESCR', 'feature_names'] #The Iris contains data about 3 types of Iris flowers namely:print iris.target_names#Let's look at the shape of the Iris datasetprint iris.data.shapeprint iris.target.shape#So there is data for 150 Iris flowers and a target set with 0,1,2 depending on the type of Iris.#Let's look at the featuresprint iris.feature_names#Great, now the objective is to learn from this dataset so given a new Iris flower we can best guess its type#Let's keep this simple to start with and train on the whole dataset. ['setosa' 'versicolor' 'virginica'](150, 4)(150,)['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] #Fitting the Iris dataset using KNNX,y = iris.data, iris.target#Fitting KNN with 1 Neighbor. This is generally a very bad idea since the 1st closest neighbor to each point is itself#so we will definitely overfit. It's equivalent to hardcoding labels for each row in the dataset.iris_knn = KNeighborsClassifier(n_ Continue reading >>

Pima: Pima Indian Diabetes Dataset In Danno11/smvcir: Sliced Mean Variance Covariance Inverse Regression

Pima: Pima Indian Diabetes Dataset In Danno11/smvcir: Sliced Mean Variance Covariance Inverse Regression

Sliced Mean Variance Covariance Inverse Regression An object of class data.frame with 768 rows and 9 columns. Class Variable: "diabetes" 0 = no diabetes, 1 = diabetes 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test 6. Body mass index (weight in kg/(height in m)^2) data ( pima ) library (caret)train<-createDataPartition( pima $diabetes, p = .8, list = F)###Create a training set using 80% of datasetpim.smv<- smvcir ("diabetes", data = pima [train,], test = T) ###Build smvcir model on training setpreds<- predict (pim.smv, newdata = pima [-train,], type = "class") table (preds, pima $diabetes[-train]) ###Check accuracy###Get Coordinatespred_coords<- predict (pim.smv, newdata = pima , coordinates_only = TRUE )pred_coords$diabetes<- pima $diabetes library (e1071)svm_mod<-svm(diabetes~., data = pred_coords[train,], kernel = "radial") ###Build an SVM model and check accuracysvmp<- predict (svm_mod, newdata = pred_coords[-train,])confusionMatrix(svmp, pred_coords$diabetes[-train], positive = "1") danno11/SMVCIR documentation built on May 12, 2017, 5:35 p.m. Continue reading >>

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