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Diabetes Dataset Weka

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 >>

Uci Machine Learning Repository: Data Sets

Uci Machine Learning Repository: Data Sets

1. 3D Road Network (North Jutland, Denmark) : 3D road network with highly accurate elevation information (+-20cm) from Denmark used in eco-routing and fuel/Co2-estimation routing algorithms. 2. AAAI 2013 Accepted Papers : This data set compromises the metadata for the 2013 AAAI conference's accepted papers (main track only), including paper titles, abstracts, and keywords of varying granularity. 3. AAAI 2014 Accepted Papers : This data set compromises the metadata for the 2014 AAAI conference's accepted papers, including paper titles, authors, abstracts, and keywords of varying granularity. 4. Abalone : Predict the age of abalone from physical measurements 5. Abscisic Acid Signaling Network : The objective is to determine the set of boolean rules that describe the interactions of the nodes within this plant signaling network. The dataset includes 300 separate boolean pseudodynamic simulations using an asynchronous update scheme. 6. Activities of Daily Living (ADLs) Recognition Using Binary Sensors : This dataset comprises information regarding the ADLs performed by two users on a daily basis in their own homes. 7. Activity Recognition from Single Chest-Mounted Accelerometer : The dataset collects data from a wearable accelerometer mounted on the chest. The dataset is intended for Activity Recognition research purposes. 8. Activity Recognition system based on Multisensor data fusion (AReM) : This dataset contains temporal data from a Wireless Sensor Network worn by an actor performing the activities: bending, cycling, lying down, sitting, standing, walking. 9. Activity recognition with healthy older people using a batteryless wearable sensor : Sequential motion data from 14 healthy older people aged 66 to 86 years old using a batteryless, wearable sensor on top of their Continue reading >>

Comparative Analysis Of Data Mining Classification Algorithms In Type-2 Diabetes Prediction Data Using Weka Approach | Ahmed | International Journal Of Science And Engineering

Comparative Analysis Of Data Mining Classification Algorithms In Type-2 Diabetes Prediction Data Using Weka Approach | Ahmed | International Journal Of Science And Engineering

Comparative Analysis of Data Mining Classification Algorithms in Type-2 Diabetes Prediction Data Using WEKA Approach DOI: The goal of this paper discusses about different types of data mining classification algorithms accuracies that are widely used to extract significant knowledge from huge amounts of data. Here illustrate 20 classifications of supervised data mining algorithms base on type-2 diabetes disease dataset perspective to Bangladeshi populations. In this paper we compare 20 classification algorithms by measuring accuracies, speed and robustness of those algorithms using WEKA toolkit version 3.6.5. Accuracies of classification algorithms are measured in 3 cases like Total Training data set, 10 fold Cross Validation and Percentage Split (66% taken). Speed (CPU Execution Time) and error rate also measured as like as accuracy. Firstly checked top perform algorithms that have best outcome for different cases and then ranked top outcomes algorithms. Finally ranked best 5 algorithms among 20 algorithms based on their accuracies. Accuracy; Classification Algorithms; Confusion Matrix; Data Mining; Error Rate; Type-2 Diabetes in Bangladesh; WEKA toolkit K. Ahmed, T. Jesmin, U. Fatima, Md. M., Abdullah-al-E., Md. Z. Rahman. (2012). Intelligent and Effective Diabetes Prediction System Using Data Mining Approach. ORIENTAL JOURNAL OF COMPUTER SCIENCE & TECHNOLOGY, 5(1):215-21; Unwin N, Whiting D, Gan D, Jacqmain O, Ghyoot G, editors. (2009). IDF Diabetes Atlas, 4th ed. Brussels: International Diabetes Federation. Frawley and Piatetsky-Shapiro. (1996). Knowledge Discovery in Databases: An Overview. The AAAI/MIT Press, Menlo Park, C.A. Hian Chye Koh and Gerald Tan. (2011). Data Mining Applications in Healthcare. Journal of Healthcare Information Management, 19 (2): 64-72; I Continue reading >>

Arff-datasets/diabetes.arff At Master Renatopp/arff-datasets Github

Arff-datasets/diabetes.arff At Master Renatopp/arff-datasets Github

% 1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \& % Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast % the onset of diabetes mellitus. In {\it Proceedings of the Symposium % on Computer Applications and Medical Care} (pp. 261--265). IEEE % The diagnostic, binary-valued variable investigated is whether the % patient shows signs of diabetes according to World Health Organization % criteria (i.e., if the 2 hour post-load plasma glucose was at least % 200 mg/dl at any survey examination or if found during routine medical % care). The population lives near Phoenix, Arizona, USA. % Results: Their ADAP algorithm makes a real-valued prediction between % 0 and 1. This was transformed into a binary decision using a cutoff of % 0.448. Using 576 training instances, the sensitivity and specificity % of their algorithm was 76% on the remaining 192 instances. % 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. ADAP is an adaptive learning % routine that generates and executes digital analogs of perceptron-like % devices. It is a unique algorithm; see the paper for details. % 7. For Each Attribute: (all numeric-valued) % 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test % 6. Body mass index (weight in kg/(height in m)^2) % 9. Class Distribution: (class value 1 is interpreted as "tested positive for Continue reading >>

Ijca - An Empirical Comparison By Data Mining Classification Techniques For Diabetes Data Set

Ijca - An Empirical Comparison By Data Mining Classification Techniques For Diabetes Data Set

Home Archives Volume 131 Number 2 An Empirical Comparison by Data Mining Classification Techniques for Diabetes Data Set IJCA solicits original research papers for the June 2018 Edition. Last date of manuscript submission is May 21, 2018. Read More An Empirical Comparison by Data Mining Classification Techniques for Diabetes Data Set International Journal of Computer Applications Foundation of Computer Science (FCS), NY, USA Nilesh Jagdish Vispute, Dinesh Kumar Sahu and Anil Rajput. Article: An Empirical Comparison by Data Mining Classification Techniques for Diabetes Data Set. International Journal of Computer Applications 131(2):6-11, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX @article{key:article, author = {Nilesh Jagdish Vispute and Dinesh Kumar Sahu and Anil Rajput}, title = {Article: An Empirical Comparison by Data Mining Classification Techniques for Diabetes Data Set}, journal = {International Journal of Computer Applications}, year = {2015}, volume = {131}, number = {2}, pages = {6-11}, month = {December}, note = {Published by Foundation of Computer Science (FCS), NY, USA}} Data mining is a process of extracting information from a dataset and transform it into understandable structure for further use, also it discovers patterns in large data sets . Data mining has number of important techniques such as preprocessing, classification. Classification is one such technique which is based on supervised learning.. diabetic is a life threatening disease prevalent in several developed as well as developing countries like India. the data classification is diabetic patients data set is developed by collecting data from hospital repository consists of 1865 instances with different attributes. The instances in the dataset are two cat Continue reading >>

Em Clustering Analysis Of Diabetes Patients Basic Diagnosis Index

Em Clustering Analysis Of Diabetes Patients Basic Diagnosis Index

EM Clustering Analysis of Diabetes Patients Basic Diagnosis Index a Pharmacy-PC Operations, University of Texas MD Anderson Cancer Center, Houston, TX b Department of Family and Community Medicine, Baylor College of Medicine, Houston, TX a Pharmacy-PC Operations, University of Texas MD Anderson Cancer Center, Houston, TX b Department of Family and Community Medicine, Baylor College of Medicine, Houston, TX Copyright This is an Open Access article: verbatim copying and redistribution ofthis article are permitted in all media for any purpose Cluster analysis can group similar instances into same group. Partitioningcluster assigns classes to samples without known the classes in advance. Mostcommon algorithms are K-means and Expectation Maximization (EM). EMclustering algorithm can find number of distributions of generatingdata and build mixture models. It identifiesgroups that are either overlapping or varying sizes and shapes. In thisproject, by using EM in Machine Learning Algorithm in JAVA (WEKA) system, diabetespatient basic diagnosis index data have been analyzedfor clustering. Diabetes is a common disease in the world, over 18 million Americans havediabetes and another 16 million have pre-diabetes. Clinical diagnosisof diabetes is often prompted by physical symptoms and abnormal labtest values. Some abnormal indexes include Body Mass Index (BMI), BloodPressure (BP). This project uses clustering tool to analyze the patients diseasediagnosis data to determine if clustering tool can be effectivelyanalyze the patients diagnosis data, to perform exploratory dataanalysis and see if generated clusters are meaningful. In regardsto the diabetes patient data, it tries to determine if the age, race, gender, BMIand BP exhibit clusters or patterns, and to attempt to dividethe da Continue reading >>

Diagnosing Diabetes With Weka & Machine Learning

Diagnosing Diabetes With Weka & Machine Learning

I also came across a book by David H. DeJong called Stealing the Gila: The Pima Agricultural Economy and Water Deprivation, 1848-1921 which describes how the diverting of water and other policies reduced [the Pima] to cycles of poverty, their lives destroyed by greed and disrespect for the law, as well as legal decisions made for personal gain. It looks like a really interesting read. The idea with this data set is to take the attributes listed above, combine them with the labelling (i.e. we know who has been diagnosed with diabetes and who hasnt) and figure out the pattern as much as we can. Can we figure out if someone is likely to have diabetes just by taking a few of these measurements? The promise of machine learning and other related statistical tools is that we can learn from the data that we have to make testing more useful. Perhaps we only need your height, genetic risk factor and skin thickness to make such a prediction? (Unlikely, but still, perhaps). If we emerge from our study with a statistical model, how well does it perform? How much can we generalise from the data? What would be an acceptable error rate in the medical context? Is it 80% or is it 99.99%? The former would save millions of dollars in test costs but would throw lots of errors; the latter would be highly accurate but it might be expensive to calculate the model. The use case for this specific case would maybe be to identify at-risk individuals who are on the way to a diagnosis of diabetes and intervene somehow. Our motivation here is clear: people dont want to be diabetic, so how early can we catch this transition? It would save governments money, expose fewer people to unnecessary tests and improve their quality of life. Im not a doctor, but to solve this problem manually would seem to req Continue reading >>

Case Study: Predicting The Onset Of Diabetes Within Five Years (part 2 Of 3)

Case Study: Predicting The Onset Of Diabetes Within Five Years (part 2 Of 3)

Case Study: Predicting the Onset of Diabetes Within Five Years (part 2 of 3) This is a guest post by Igor Shvartser, a clever young student I have been coaching. This post is part 2 in a 3 part series on modeling the famous Pima Indians Diabetes dataset (update: download from here ). In Part 1 we defined the problem and looked at the dataset, describing observations from the patterns we noticed in the data. In thiswe will introduce the methodology, spot checking algorithms, and review initial 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. Analysis and data processing in the study was carried out using the Weka machine learning software . A ten-fold cross-validation was used for experiments. This works in the following way: Produce 10 equal sized data sets from given data Divide each set into two groups: 90% for training and10% for testing. Produce a classifier with an algorithm from 90% labeled data and apply that on the 10% testing data for set 1. Average the performance of 10 classifiers produced from 10 equal sized (training and testing) sets These algorithms are relevant because they perform classification on a dataset, deal appropriately with missing or erroneous data, and have some kind of significance in scientific articles focused on medical diagnosis, see the papers Machine Learning for Medical Diagnosis: History, State of the Art, and Perspective and Artificial Neural Networks in Medical Diagnosis . Logistic Regression is a probabilistic, statistical classifier used to predict the outcome of a categorical dependent variable based on one or more predictor variables. The algorithm measures the relatio Continue reading >>

Analysis Of Diabetes Data Set Of Pima Indians Using Neural Network And Nn Ensemble

Analysis Of Diabetes Data Set Of Pima Indians Using Neural Network And Nn Ensemble

Analysis of Diabetes data set of Pima Indians using Neural Network and NN Ensemble Data Science Professional | Hadoop & Cloud Solutions Expert Data set can be downloaded from UCI Machine Learning Repository. This data set contains of female patients (PIMA Indians) with at least 21 years of age. It has 768 instances and the following 8 attributes (All numeric-valued): 1. Number of times pregnant2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test3. Diastolic blood pressure (mm Hg)4. Triceps skin fold thickness (mm)5. 2-Hour serum insulin (mu U/ml)6. Body mass index (weight in kg/(height in m)^2)7. Diabetes pedigree function8. Age (years)9. Class variable (0 or 1) This data set contains the diagnostic data to investigate whether the patient shows signs of diabetes according to World Health Organization criteria such as the 2-hour post-load plasma glucose. The graph below (obtained from Weka) shows the histograms of all the attributes. The above histograms provide the following insights: Class 0 with 500 instances represents patients who tested negative and class 1 with 268 instances represents the patients tested positive. Data set is small and seems to be biased with almost 65 percent patients testing negative. This could act as a limitation in the study. Attributes 2-Hour serum insulin, Diabetes Pedigree function, Age and Number of times pregnant are highly skewed to the right. While Plasma glucose concentration, Diastolic Blood pressure and Body Mass Index appear to be normally distributed. Removal of the outliers: As seen in histogram below there are 49 outliers (red bar) which have been removed as part of data pre-processing. Reviewing scatter plots below of all attributes did not show with relationships amongst the attributes, however, there Continue reading >>

Survey On Clinical Prediction Models For Diabetes Prediction

Survey On Clinical Prediction Models For Diabetes Prediction

Predictive analytics has gained a lot of reputation in the emerging technology Big data. Predictive analytics is an advanced form of analytics. Predictive analytics goes beyond data mining. A huge amount of medical data is available today regarding the disease, their symptoms, reasons for illness, and their effects on health. But this data is not analysed properly to predict or to study a disease. The aim of this paper is to give a detailed version of predictive models from base to state-of-art, describing various types of predictive models, steps to develop a predictive model, their applications in health care in a broader way and particularly in diabetes. Predictive analyticsDiabetesClinical prediction modelsTraditional modelHybrid modelMachine learning Predictive analytics use statistical or machine learning method to make a prediction about future or unknown outcomes [ 1 ]. It uses text mining for unstructured data, answers the question what is next step? It uses historical and present data to predict future regarding activity, behaviour and trends. To do this it makes use of statistical analysis techniques, analytical queries and automated machine learning algorithms. Predictive analytics need experts to build predictive models. These models are used for prediction. There are many applications of predictive analytics, out of which one is health care. A most common disease now a days is diabetes. People are suffering with it and the patient number increases day by day. The World Health Organization (WHO) predicts that by 2030 there will be approximately 350 million people worldwide affected by diabetes [ 2 , 3 ]. Mostly whatever food we eat is converted into glucose or sugar. Now, this glucose or sugar is used for energy. Glucose is transported to body cells throug Continue reading >>

A Decision Tree Is Built With Weka To Classify Pat... | Chegg.com

A Decision Tree Is Built With Weka To Classify Pat... | Chegg.com

home / study / business / finance / finance questions and answers / A Decision Tree Is Built With WEKA To Classify Patients Into Positive Or Negative For Diabetes ... Question: A decision tree is built with WEKA to classify patients intopositive or negative for diabetes on... A decision tree is built with WEKA to classify patients intopositive or negative for diabetes on the following dataset fromNational Institute of Diabetes and Digestive and Kidney Diseases.The dataset has 4 attributes: class (the true diagnosis): negativeor positive plas: Plasma glucose concentration a 2 hours in an oralglucose tolerance test mass: Body mass index (weight in kg/(heightin m)^2) age: Age (years) The tree is shown below. === Classifiermodel (full training set) === J48 pruned tree -----------------plas <= 127: negative (485.0/94.0) plas > 127 | mass <=29.9 | | plas <= 145: negative (41.0/6.0) | | plas > 145 | || age <= 25: negative (4.0) | | | age > 25 | | | | age <=61: positive (27.0/9.0) | | | | age > 61: negative (4.0) | mass> 29.9 | | plas <= 157 | | | age <= 30: negative(50.0/23.0) | | | age > 30: positive (65.0/18.0) | | plas >157: positive (92.0/12.0) Number of Leaves : 8 Size of the tree :15 a. Use the WEKA output to construct a confusion matrix. (Hint:look at each leaf node to determine how many instances fall intoeach of the four quadrants; and aggregate results of all leaf nodesto obtain the final counts) (8%) bi_test1 pic.png TP=? FP=? FN=?TN=? b. In medical diagnosis, three metrics are commonly used:sensitivity, specificity and diagnosis accuracy. Sensitivity isdefined as TP/(TP+FN) ; Specificity is defined as TN/(FP+TN);Diagnosis Accuracy is defined as the average of Sensitivity andSpecificity. Calculate the Diagnosis Accuracy based on theconfusion matrix above. (2%) Continue reading >>

Wekapyscript: Classification, Regression, And Filter Schemes For Weka Implemented In Python

Wekapyscript: Classification, Regression, And Filter Schemes For Weka Implemented In Python

WekaPyScript: Classification, Regression, and Filter Schemes for WEKA Implemented in Python WekaPyScript is a package for the machine learning software WEKA that allows learning algorithms and preprocessing methods for classification and regression to be written in Python, as opposed to WEKAs implementation language, Java. This opens up WEKA to its machine learning and scientific computing ecosystem. Furthermore, due to Pythons minimalist syntax, learning algorithms and preprocessing methods can be prototyped easily and utilised from within WEKA. WekaPyScript works by running a local Python server using the hosts installation of Python; as a result, any libraries installed in the host installation can be leveraged when writing a script for WekaPyScript. Three example scripts (two learning algorithms and one preprocessing method) are presented. WEKA [ 1 ] is a popular machine learning workbench written in Java that allows users to easily classify, process, and explore data. There are many ways WEKA can be used: through the WEKA Explorer, users can visualise data, train learning algorithms for classification and regression and examine performance metrics; in the WEKA Experimenter, datasets and algorithms can be compared in an automated fashion; or, it can simply be invoked on the terminal or used as an external library in a Java project. Another machine learning library that is increasingly becoming popular is Scikit-Learn [ 2 ], which is written in Python. Part of what makes Python attractive is its ease of use, minimalist syntax, and interactive nature, which makes it an appealing language to learn for non-specialists. As a result of Scikit-Learns popularity the wekaPython [ 3 ] package was released, which allows users to build Scikit-Learn classifiers from within WEKA Continue reading >>

Performance Analysis Of Different Classification Methods In Data Mining For Diabetes Dataset Using Weka Tool

Performance Analysis Of Different Classification Methods In Data Mining For Diabetes Dataset Using Weka Tool

Performance Analysis of Different Classification Methods in Data Mining for Diabetes Dataset Using WEKA Tool Data mining is the process of analyzing data based on different perspectives and summarizing it into useful information. Classification is one of the generally used techniques in medical data mining. The goal here is to discover new patterns to provide meaningful and useful information for the users. Recently data mining techniques are applied to healthcare datasets to explore suitable methods and techniques and to extract useful patterns. This paper includes implementation of different classification methods, measures, analysis and comparison pertaining to diabetes dataset. A detailed performance analysis and comparative study of these methods are done, which can be further used to choose the appropriate algorithm for future analysis for the given dataset. International Journal on Recent and Innovation Trends in Computing and Communication Performance Analysis of Different Classification Methods in Data Mining for Abstract Data mining is the process of analyzing data based on different perspectives and summarizing it into useful information. Classification is one of the generally used techniques in medical data mining. The goal here is to discover new patterns to provide meaningful and useful information for the users. Recently data mining techniques are applied to healthcare datasets to explore suitable methods and techniques and to extract useful patterns. This paper includes implementation of different classification methods, measures, analysis and comparison pertaining to diabetes dataset. A detailed performance analysis and comparative study of these methods are done, which can be further used to choose the appropriate algorithm for future analysis for the Continue reading >>

Running The Diabetes Experiment

Running The Diabetes Experiment

In the Pima Indians Diabetes experiment, the goal is to compare threeapproaches to fitting a model: A model found by a "hill climbing" search of the space ofBayesian networks We would like to evaluate these models on small and large data sets tosee if they give different results. Download the diabetes.arff data fileand save it in the weka-3-4/data folder. These models require that the data be discretized. For ourexperiment, we will discretize each input variable into 3 ranges("low", "medium", "high") by using an automated algorithm. Thisalgorithm does not analyze the class variable (i.e., whether theperson has or does not have diabetes). Here is the procedure: Start WEKA. You will see the weka startup window: Click on the Explorer button. This will bring up the main screen: Load the diabetes data by clicking on "Open file...", navigatingto the data folder, and selectingdiabetes.arff. The main screen should now look likethis: Click on "Choose" in the "Filter" section. This will pop up thefollowing menu: Click on the "+" beside "filters". Then click on the "+" beside"unsupervised". Then click on the "+" beside "attributes", andfinally, click on "Discretize". The main screen should now showDiscretize -B 10 -M -1.0 -R first-last in the area nextto the "Choose" button. Click on the "Discretize" text in this box. The following windowshould pop up: Set bins to be "3" and set "useEqualFrequency" to be "true". Thenclick "OK". Click the "Apply" button on the right-hand side of the mainwindow. Your window should now look like this: The attributes have now been discretized. Now click on the "Classify" tab at the top of the window. Thiswill change to the classification page. It should look like this: Now click on the "Choose" button in the "Classifier" section.In the menu that pops Continue reading >>

Standard Machine Learning Datasets To Practice In Weka

Standard Machine Learning Datasets To Practice In Weka

Standard Machine Learning Datasets To Practice in Weka It is a good idea to have small well understood datasets when getting started in machine learning and learning a new tool. The Weka machine learning workbench provides a directory of small well understood datasets in the installed directory. In this post you will discover some of these small well understood datasets distributed with Weka, their details and where to learn more about them. We will focus on a handful of datasets of differing types. After reading this post you will know: Where the sample datasets are located or where to download them afresh if you need them. Specific standard datasets you can use to explore different aspects of classification and regression predictive models. Where to go for more information about specific datasets and state of the art results. Standard Machine Learning Datasets Used For Practice in Weka Photo by Marvin Foushee , some rights reserved. An installation of the open source Weka machine learning workbench includes a data/ directory full of standard machine learning problems. This is very useful when you are getting started in machine learning or learning how to get started with the Weka platform. It provides standard machine learning datasets for common classification and regression problems, for example, below is a snapshot from this directory: Provided Datasets in Weka Installation Directory All datasets are in the Weka native ARFF file format and can be loaded directly into Weka, meaning you can start developing practice models immediately. There are some special distributions of Weka that may not include the data/ directory. If you have chosen to install one of these distributions, you can download the .zip distribution of Weka , unzip it and copy the data/ directory to Continue reading >>

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