scholarly journals Detecting Kidney Disease using Naïve Bayes and Decision Tree in Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 498-501
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Naive Bayes ◽  
Kidney Diseases ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Machine Learning Classification ◽  
The Relationship

Chronic Kidney Disease (CKD) mostly influence patients suffered from difficulties due to diabetes or high blood pressure and make them unable to carry out their daily activities. In a survey , it has been revealed that one in 12 persons living in two biggest cities of India diagnosed of CKD features that put them at high risk for unfavourable outcomes. In this article, we have analyzed as well as anticipated chronic kidney disease by discovering the hidden pattern of the relationship using feature selection and Machine Learning classification approach like naive Bayes classifier and decision tree(J48). The dataset on which these approaches are applied is taken from UC Irvine repository. Based on certain feature, the approaches will predict whether a person is diagnosed with a CKD or Not CKD. While performing comparative analysis, it has been observed that J48 decision tree gives high accuracy rate in prediction. J48 classifier proves to be efficient and more effective in detecting kidney diseases.

scholarly journals Count Vectorized Spam and Ham Discernment of Short Message Service using Machine Learning Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 557-561
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Gradient Boosting ◽  
Svm Classifier ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

With the growing volume and the amount of spam message, the demand for identifying the effective method for spam detection is in claim. The growth of mobile phone and Smartphone has led to the drastic increase in the SMS spam messages. The advancement and the clean process of mobile message servicing channel have attracted the hackers to perform their hacking through SMS messages. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the owners. With this background, this paper focuses on predicting the Spam SMS messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of Spam message detection is achieved in four ways. Firstly, the distribution of the target variable Spam Type the dataset is identified and represented by the graphical notations. Secondly, the top word features for the Spam and Ham messages in the SMS messages is extracted using Count Vectorizer and it is displayed using spam and Ham word cloud. Thirdly, the extracted Counter vectorized feature importance SMS Spam Message detection dataset is fitted to various classifiers like KNN classifier, Random Forest classifier, Linear SVM classifier, Ada Boost classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Multinomial Naive Bayes classifier have achieved the effective prediction with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.20%..

scholarly journals Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Amirhessam Tahmassebi ◽  
Amir H. Gandomi ◽  
Mieke H. J. Schulte ◽  
Anna E. Goudriaan ◽  
Simon Y. Foo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Decision Tree ◽  
Data Reduction ◽  
Resting State ◽  
Naive Bayes ◽  
Resting State Fmri ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Bayes Classifier

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future.

scholarly journals NAIVE BAYES CLASSIFIER, DECISION TREE AND ADABOOST ENSEMBLE ALGORITHM – ADVANTAGES AND DISADVANTAGES

2020 ◽  
Author(s):  
Neli Kalcheva ◽  
◽  
Maya Todorova ◽  
Ginka Marinova ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Universal Method ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Advantages And Disadvantages ◽  
Ensemble Algorithm

The purpose of the publication is to analyse popular classification algorithms in machine learning. The following classifiers were studied: Naive Bayes Classifier, Decision Tree and AdaBoost Ensemble Algorithm. Their advantages and disadvantages are discussed. Research shows that there is no comprehensive universal method or algorithm for classification in machine learning. Each method or algorithm works well depending on the specifics of the task and the data used.

scholarly journals Chronic Kidney Disease Prediction using Machine Learning Algorithms

2021 ◽  
Vol 1 (3) ◽  
pp. 1-4
Author(s):  
Kallu Samatha ◽  
Muppidi Rohitha Reddy ◽  
Pattan Faizal Khan ◽  
Rayapati Akhil Chowdary ◽  
P.V.R.D Prasada Rao
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Kidney Diseases ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Tree Classifier

Kidney diseases are increasing day by day among people. It is becoming a major health issue around the world. Not maintaining proper food habits and drinking less amount of water are one of the major reasons that contribute this condition. With this, it has become necessary to build up a system to foresee Chronic Kidney Diseases precisely. Here, we have proposed an approach for real time kidney disease prediction. Our aim is to find the best and efficient machine learning (ML) application that can effectively recognize and predict the condition of chronic kidney disease. We have used the data from UCI machine learning repository. In this work, five important machine learning classification techniques were considered for predicting chronic kidney disease which are KNN, Logistic Regression, Random Forest Classifier, SVM and Decision Tree Classifier. In this process, the data has been divided into two sections. In one section train dataset got trained and another section got evaluated by test dataset. The analysis results show that Decision Tree Classifier and Logistic Regression algorithms achieved highest performance than the other classifiers, obtaining the accuracy of 98.75% followed by random Forest, which stands at 97.5%.

scholarly journals Chronic Kidney Disease Prediction using Machine Learning Algorithms

2021 ◽  
pp. 1-4
Author(s):  
Ms. Kallu Samatha ◽  
◽  
Ms. Muppidi Rohitha Reddy ◽  
Mr. Pattan Faizal Khan ◽  
Mr. Rayapati Akhil Chowdary ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Logistic Regression ◽  
Random Forest ◽  
Kidney Disease ◽  
Decision Tree ◽  
Kidney Diseases ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Tree Classifier

Kidney diseases are increasing day by day among people. It is becoming a major health issue around the world. Not maintaining proper food habits and drinking less amount of water are one of the major reasons that contribute this condition. With this, it has become necessary to build up a system to foresee Chronic Kidney Diseases precisely. Here, we have proposed an approach for real time kidney disease prediction. Our aim is to find the best and efficient machine learning (ML) application that can effectively recognize and predict the condition of chronic kidney disease. We have used the data from UCI machine learning repository. In this work, five important machine learning classification techniques were considered for predicting chronic kidney disease which are KNN, Logistic Regression, Random Forest Classifier, SVM and Decision Tree Classifier. In this process, the data has been divided into two sections. In one section train dataset got trained and another section got evaluated by test dataset. The analysis results show that Decision Tree Classifier and Logistic Regression algorithms achieved highest performance than the other classifiers, obtaining the accuracy of 98.75% followed by random Forest, which stands at 97.5%.

Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat

2021 ◽  
pp. 1098612X2110012
Author(s):  
Jade Renard ◽  
Mathieu R Faucher ◽  
Anaïs Combes ◽  
Didier Concordet ◽  
Brice S Reynolds
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Diagnostic Performance ◽  
Clinical Signs ◽  
Learning System ◽  
Clinical Severity ◽  
Medium Term ◽  
Term Survival

Objectives The aim of this study was to develop an algorithm capable of predicting short- and medium-term survival in cases of intrinsic acute-on-chronic kidney disease (ACKD) in cats. Methods The medical record database was searched to identify cats hospitalised for acute clinical signs and azotaemia of at least 48 h duration and diagnosed to have underlying chronic kidney disease based on ultrasonographic renal abnormalities or previously documented azotaemia. Cases with postrenal azotaemia, exposure to nephrotoxicants, feline infectious peritonitis or neoplasia were excluded. Clinical variables were combined in a clinical severity score (CSS). Clinicopathological and ultrasonographic variables were also collected. The following variables were tested as inputs in a machine learning system: age, body weight (BW), CSS, identification of small kidneys or nephroliths by ultrasonography, serum creatinine at 48 h (Crea48), spontaneous feeding at 48 h (SpF48) and aetiology. Outputs were outcomes at 7, 30, 90 and 180 days. The machine-learning system was trained to develop decision tree algorithms capable of predicting outputs from inputs. Finally, the diagnostic performance of the algorithms was calculated. Results Crea48 was the best predictor of survival at 7 days (threshold 1043 µmol/l, sensitivity 0.96, specificity 0.53), 30 days (threshold 566 µmol/l, sensitivity 0.70, specificity 0.89) and 90 days (threshold 566 µmol/l, sensitivity 0.76, specificity 0.80), with fewer cats still alive when their Crea48 was above these thresholds. A short decision tree, including age and Crea48, predicted the 180-day outcome best. When Crea48 was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF48 and identification of small kidneys with an overall diagnostic performance similar to that using Crea48. Conclusions and relevance Crea48 helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF48 and identification of small kidneys.

scholarly journals Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Qingchao Liu ◽  
Jian Lu ◽  
Shuyan Chen ◽  
Kangjia Zhao
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Classifier Ensemble ◽  
Optimal Threshold ◽  
Incident Detection ◽  
Bayes Classifier ◽  
Traffic Incident ◽  
Better Than

This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection. The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results. However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process. To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection. In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents. In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree. The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators. More importantly, the performances of the NB classifier ensemble are very stable.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

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