Diabetes diagnosis system using modified Naive Bayes classifier (original) (raw)
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Prediction of Heart Disease in Diabetic patients using Naive Bayes Classification Technique
International Journal of Computer Applications Technology and Research, 2018
The objective of our paper is to predict the risk of heart disease in diabetic patients. In this research paper we are applying Naive Bayes data mining classification technique which is a probabilistic classifier based on Bayes theorem with strong (naive) independence assumptions between the features. Data mining techniques have been widely used in health care systems for prediction of various diseases with accuracy. Health care industry contains large amount of data and hidden information. Effective decisions are made with this hidden information by applying data mining techniques. These techniques are used to discover hidden patterns and relationships from the datasets. The major challenge facing the healthcare industry is the provision for quality services at affordable costs. A quality service implies diagnosing patients correctly and treating them effectively. In this proposed system certain attributes are consider in diabetic patients to predict the risk of heart disease
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
This work is dedicated to patients specially, rural patients can get to know the early stage detection of diseases before laboratory tests reducing the unlimited waiting time and cost expenditure. Clinical Decision Support System (CDSS) can be used for analyzing diseases to predict almost accurate disease automatically and patient’s query. This work has been done with the help of a doctor as a human expert. We collected 300 sample data from patients. We have made the dataset from our sample patient’s information. Naïve Bayes classifier is used here to classify the diseases easily. The selected diseases are Malaria, Tuberculosis, Stroke, Fever, Diabetes, Heart disease. The prediction of a disease is measured with the prediction of a doctor before laboratory tests to get the system’s accuracy. Here we got 100% accuracy on the trained dataset containing 180 cases
Optimization The Naive Bayes Classifier Method to diagnose diabetes Mellitus
IAIC Transactions on Sustainable Digital Innovation, 2019
World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus. After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29%
Diabetes disease prediction system using HNB classifier based on discretization method
Journal of Integrative Bioinformatics
Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.
Real Time Diabetes Prediction using Naïve Bayes Classifier on Big Data of Healthcare
2020
1,2,3U.G. Students, Department of Information Technology, TSEC College, Mumbai, Maharashtra, India 4Professor and Head of Department , Examination Incharge, Department of Information Technology, TSEC College, Mumbai, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Diabetes is a chronic disease, with numerous cases enrolled annually. The number of deaths caused by diabetes has been expanding each year, and it is crucial to anticipate the factor so that they can be relieved at the soonest guaranteeing the patient's life is saved. This prediction is effectively acquired by utilizing Naïve Bayes Classifier. This algorithm classifies, based on the indications of whether an individual has diabetes or not. This model achieved an accuracy of around 81%. The proposed system also supports live streaming of data input, where the results are obtained in real-time for the e...
Diabetes means blood sugar is above desired level on a sustained basis. The prime objective of this research work is to provide a better classification of diabetes. There are already several existing method, which have been implemented for the classification of diabetes dataset. In medical sector, the classifications systems have been widely used to exploit the patient's data and make the predictive models or build set of rules. In this manuscript firstly NBs used for the classification on all the attributes and then GA used as an attribute selection and NBs used on that selected attribute for classification. The experimental results show the performance of this work on PIDD and provide better classification for diagnosis. 2.1 Used diabetes disease dataset The Pima Indian Diabetes Dataset (PIDD) has been taken from the UCI Machine Learning repository. The same dataset used in the reference (Polat
A Machine Learning-Based Intelligent System for Predicting Diabetes
International Journal of Big Data and Analytics in Healthcare, 2019
In this era of technological growth, the diagnosis of diseases and finding cures, personal health parameter management and predicting the possibility of susceptibility to some diseases have become accessible and easy. Although all over the world millions of people are falling victim to diabetes, in most of the cases they are not even aware of their situation due to the silent nature of diabetes. Therefore, the objective of this research is to propose an intelligent system based on a machine learning algorithm to improve the accuracy of predicting diabetes. To attain this objective, an algorithm was proposed based on Naïve Bayes with prior clustering. Second, the performance of the proposed algorithm was evaluated using 532 data related to diabetic patients. Finally, the performance of the existing Naïve Bayes algorithm was compared with the proposed algorithm. The results of the comparative study showed that the improvement in the accuracy has been made apparent for the proposed alg...
Prediction of Heart Disease and Diabetes Using Naive Bayes Algorithm
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
Based on the test report values, diagnose a potential problem. The patient's report can be entered as feedback by the doctors (Sugar level, Age, Blood pressure, etc.). Through evaluating the available data collection, we can predict whether the patient has heart disease or diabetes using the method. Apart from that, we use Rstudio's R shiny addon for Web UI design. As a coding language, we use the R programming language. The Rstudio IDE was used. The datasets were obtained from the University of California at Irvine's repository.
Diabetes Prediction: A Study of Various Classification based Data Mining Techniques
International Journal of Computer Science and Informatics, 2022
Data Mining is an integral part of KDD (Knowledge Discovery in Databases) process. It deals with discovering unknown patterns and knowledge hidden in data. Classification is a pivotal data mining technique with a very wide range of applications. Now a day’s diabetic has become a major disease which has almost crippled people across the globe. It is a medical condition that causes the metabolism to become dysfunctional and increases the blood sugar level in the body and it becomes a major concern for medical practitioner and people at large. An early diagnosis is the starting point for living well with diabetes. Classification Analysis on diabetic dataset is a part of this diagnosis process which can help to detect a diabetic patient from non-diabetic. In this paper classification algorithms are applied on the Pima Indian Diabetic Database which is collected from UCI Machine Learning Laboratory. Various classification algorithms which are Naïve Bayes Classifier, Logistic Regression, ...
Disease Prediction System using naïve bayes
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Accurate and on-time analysis of any health-related problem is vital for the prevention and treatment of the illness. The standard way of diagnosis might not be sufficient. Developing a diagnosis system with machine learning (ML) algorithms for prediction of any disease can helpin a very more accurate diagnosis than the traditional method. The proposed model is an Disease Prediction System with the help of machine learning algorithm Naive Bayes which takes the symptoms as the input and it gives the output as predicted disease. It results in saving time and also makes it easy to induce a warning about your health before it's too late. By using this model anyone can get the result as predicted disease by simply given the symptoms as input. The accuracy of this model is more than existing models.