Automation of Diabetics Prediction System using Data Mining Technique (original) (raw)

A Data Mining Approach for Prediction and Treatment Ofdiabetes Disease

2014

The advancement in computers provided large amount of data. The task is to analyse the input data and obtain the required data which can be done by various data mining techniques. The diagnosis of diabetes is a significant and tedious task in medicine. So the present work focus on analysis of diabetes data by various data mining techniques which involve,Naive Bayes, J48(C4.5) JRip ,Neural networks, Decision trees, KNN, Fuzzy logic and Genetic Algorithms based on accuracy and time. The 9 selected attributes wereSex, Diastolic B.P, Plasma glucose, Skin fold thick, BMI, Diabetes Pedigree type, No. of times Pregnant, 2 hr Serum Insulin and Diabetes probability.J48(C4.5) reported simple, efficient classifier of diabetes data.

Diabetic Prediction System Using Data Mining

Proceedings in Computing, 9th International Research Conference-KDU, 2016

Diabetes is one of deadliest diseases in the world. As per the existing system in Sri Lanka, patients have to visit a diagnostic center, consult their doctor and wait for a day or more to get their result. Moreover, every time they want to get their diagnosis report, they have to waste their money in vain. But with the rise of Machine Learning approaches, we have been able to find a solution to this problem using data mining. Data mining is one of the key areas of Machine learning. It plays a significant role in diabetes research because It has the ability to extract hidden knowledge from a huge amount of diabetes related data. The aim of this research is to develop a system which can predict whether the patient has diabetes or not. Furthermore, predicting the disease early leads to treatment of the patients before it becomes critical. This research has focused on developing a system based on three classification methods namely, Decision Tree, Naïve Bayes and Support Vector Machine algorithms. Currently, the models give accuracies of 84.6667%, 76.6667%, and 77.3333% for Decision Tree, Naïve Bayes, and SMO Support Vector Machine respectively. These results have been verified using Receiver Operating Characteristic curves in a cost-sensitive manner. The developed ensemble method uses votes given by the other algorithms to produce the final result. This voting mechanism eliminates the algorithm dependent misclassifications. Results show a significant improvement of accuracy of ensemble method compares to other methods.

Review on Prediction of Diabetes Mellitus using Data Mining Technique

International Journal of Engineering and Technical Research (IJETR), 2018

Data mining plays a vital role in prediction of diseases in health care industry. Diabetes is one of the major health issues in the world. According to World Health Organization 2014 report, around 422 million people worldwide are suffering from diabetes. Diabetes is considered as one of deadliest and chronic disease which causes an increase in blood sugar. Many complications occur if diabetes remains untreated and unidentified. Data mining is a process of obtaining the information from a dataset and transforms it into unambiguous structure. Medical Data mining techniques are used to find hidden patterns in the data sets of medical domain for medical diagnosis and treatment. There are various data mining techniques for prediction of diseases like heart diseases, cancer, and kidney etc. Prediction of diabetes is a fastest growing technology. This paper helps in predicting polygenic disorder by applying data processing techniques. Using various data mining techniques we can predict Diabetes from the data set of a patient. This paper concentrates on the overall survey related to data mining techniques for predicting diabetes.

Data Mining Techniques Based Diabetes Prediction

Indian Journal of Artificial Intelligence and Neural Networking, 2021

Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world's greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, cl...

DIABETES MELLITUS PREDICTION SYSTEM USING DATA MINING

Now a days detection of patients with elevated risk of diabetes mellitus is developing critical to the improved prevention and overall health management of these patients. We aim to apply association rule mining to electronic medical records (EMR) to invent sets of risk factors and their corresponding subpopulations that represent patients which have high risk of developing diabetes. With the high dimensionality of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy medical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, advantages and drawbacks. We proposed extensions to incorporate risk of diabetes into the process of finding an optimum summary. We evaluated these modified techniques on a real-world border line diabetes patient associate. We found that all four methods gives summaries that described subpopulations at high risk of diabetes with every method having its clear strength. In this extension to the Bottom-Up Summarization (BUS) [1] algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.

Implementation of Data Mining Algorithms for Diabetes Prediction

The process of analyzing different aspects of data and aggregating it into useful information is called data mining. The goal is to provide meaningful and useful information for the users about the diabetes. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This research project aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Naïve Bayes algorithms. The monitoring module analyzes the laboratory test reports of the blood sugar levels of the patient and provides proper awareness messages to the patient through mail and bar chart.

Diabetes prediction using data mining techniques

International Journal of Research and Innovation in Applied Science, 2019

This research work was conducted on the design and implementation of a diabetes prediction system, a case study of Fudawa Health Centre. This research will help in automating prediction of diabetes even before clinicians arrived. The current process of carrying this activity is manually which tends not to analyzing data flexible for the doctors, and transmission of information is not transparent. The system was design using Java Programming Language, Weka Tool, and MySQL (Microsoft Structured Query Language) as the back end and a strategic approach to analyse the existing system was taking in order to meets the demands of this system and solve the problems of the existing system by implementing the naïve beyes classifier. The implementation of this new system will help to reduce the stressful process, doctors' face during prediction of diabetes, the result of the experiment shows that the proposed system has a better prediction in terms of accuracy.

Study of Data Mining Algorithms for Prediction and Diagnosis of Diabetes Mellitus

Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various available traditional methods for diagnosing diabetes are based on physical and chemical tests. These methods can have errors due to different uncertainties. A number of Data mining algorithms were designed to overcome these uncertainties. Among these algorithms, amalgam KNN and ANFIS provides higher classification accuracy than the existing approaches. The main data mining algorithms discussed in this paper are EM algorithm, KNN algorithm, K-means algorithm, amalgam KNN algorithm and ANFIS algorithm. EM algorithm is the expectation-maximization algorithm used for sampling, to determine and maximize the expectation in successive iteration cycles. KNN algorithm is used for classifying the objects and used to predict the labels based on some closest training examples in the feature space. K means algorithm follows partitioning methods based on some input parameters on the datasets of n objects. Amalgam combines both the features of KNN and K means with some additional processing. ANFIS is the Adaptive Neuro Fuzzy Inference System which combines the features of adaptive neural network and Fuzzy Inference System. The data set chosen for classification and experimental simulation is based on Pima Indian Diabetic Set from University of California, Irvine (UCI) Repository of Machine Learning databases.

Review on Prediction of Diabetes using Data Mining Technique

Data mining plays an efficient role in prediction of diseases in health care industry. Diabetes is one of the major global health problems. According to WHO 2014 report, around 422 million people worldwide are suffering from diabetes. Diabetes is a metabolic disease where the improper management of blood glucose levels led to risk of many diseases like heart attack, kidney disease, eye etc. Many algorithms are developed for prediction of diabetes and accuracy estimation but there is no such algorithm which will provide severity in terms of ratio interpreted as impact of diabetes on different organs of human body. This paper gives detailed review of existing data mining methods used for prediction of diabetes. It also gives future direction for severity estimation of diabetes

Prediction of Diabetes Disease Using Machine Learning Model

Lecture Notes in Electrical Engineering

As per the statistics mentioned by the world health organization, four hundred twenty-two million people in the world are suffering from diabetes which has raised the death toll to 1.6 million per year. This unprecedented growth in the number of cases and the number of casualties has led to an alarming situation because the data statistics represent a significant increase in diabetic cases among the young population, 18 years of age. Diabetes leads to various health hazards such as dysfunction of the kidney, cardiovascular problems, lower limb dismembering, and retinopathy. This article builds up a model for the prediction of diabetes using machine learning. The supervised machine learning algorithms used for prediction model such as decision tree, Naïve Bayes, artificial neural network, and logistic regression. Further, the comparison of these methods has been done based on various performance parameters such as accuracy, recall, precision, and F-score. Keywords Machine Learning(ML) • Artificial neural network (ANN) • Logistic regression • Decision tree • Artificial intelligence (AI) • Naive bayes