Prediction of Heart Disease Using Different Classification Techniques

2017 ◽  
Vol 2 (2) ◽  
pp. 68-76 ◽  
Author(s):  
Sonam Nikhar ◽  
A.M. Karandikar
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Bayesian Classifier ◽  
Decision Tree Classifier ◽  
Data Mining Technique ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Tree Classifier ◽  
Better Than

Data mining is one of the essential areas of research that is more popular in health organization. Heart disease is the leading cause of death in the world over the past 10 years. The healthcare industry gathers enormous amount of heart disease data which are not “mined” to discover hidden information for effective decision making. This research intends to provide a detailed description of Naïve Bayes, decision tree classifier and Selective Bayesian classifier that are applied in our research particularly in the prediction of Heart Disease. It is known that Naïve Bayesian classifier (NB) works very well on some domains, and poorly on some. The performance of NB suffers in domains that involve correlated features. C4.5 decision trees, on the other hand, typically perform better than the Naïve Bayesian algorithm on such domains. This paper describes a Selective Bayesian classifier (SBC) that simply uses only those features that C4.5 would use in its decision tree when learning a small example of a training set, a combination of the two different natures of classifiers. Experiments conducted on Cleveland datasets indicate that SBC performs reliably better than NB on all domains, and SBC outperforms C4.5 on this dataset of which C4.5 outperform NB. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classifier and experiment also reveals that selective Bayesian classifier has a better accuracy as compared to other classifiers.

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

Classification Methods in the Detection of New Suspicious Emails

2008 ◽  
Vol 07 (03) ◽  
pp. 209-217 ◽  
Author(s):  
S. Appavu Alias Balamurugan ◽  
G. Athiappan ◽  
M. Muthu Pandian ◽  
R. Rajaram
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Binary Tree ◽  
Experimental Results ◽  
Classification Methods ◽  
Detection Rates ◽  
Naive Bayesian ◽  
The Past ◽  
Naïve Bayesian

Email has become one of the fastest and most economical forms of communication. However, the increase of email users has resulted in the dramatic increase of suspicious emails during the past few years. This paper proposes to apply classification data mining for the task of suspicious email detection based on deception theory. In this paper, email data was classified using four different classifiers (Neural Network, SVM, Naïve Bayesian and Decision Tree). The experiment was performed using weka on the basis of different data size by which the suspicious emails are detected from the email corpus. Experimental results show that simple ID3 classifier which make a binary tree, will give a promising detection rates.

Heart Disease Prediction System based on Decision Tree Classifier

2019 ◽  
Vol 11 (10-SPECIAL ISSUE) ◽  
pp. 1232-1237
Author(s):  
B. Bavani ◽  
S. Nirmala Sugirtha Rajini ◽  
M.S. Josephine ◽  
V. Prasannakumari
Keyword(s):  
Heart Disease ◽  
Decision Tree ◽  
Disease Prediction ◽  
Prediction System ◽  
Decision Tree Classifier ◽  
Tree Classifier

Naïve Bayesian Classification of Uncertain Objects Based on the Theory of Interval Probability

2016 ◽  
Vol 25 (03) ◽  
pp. 1650012 ◽  
Author(s):  
Hongmei Chen ◽  
Weiyi Liu ◽  
Lizhen Wang
Keyword(s):  
Data Mining ◽  
Uncertain Data ◽  
Bayesian Classification ◽  
Bayesian Classifier ◽  
Interval Probability ◽  
Naïve Bayesian Classifier ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Intuitive Concept

The potential applications and challenges of uncertain data mining have recently attracted interests from researchers. Most uncertain data mining algorithms consider aleatory (random) uncertainty of data, i.e. these algorithms require that exact probability distributions or confidence values are attached to uncertain data. However, knowledge about uncertainty may be incomplete in the case of epistemic (incomplete) uncertainty of data, i.e. probabilities of uncertain data may be imprecise, coarse, or missing in some applications. The paper focuses on uncertain data which miss probabilities, specially, value-uncertain discrete objects which miss probabilities (for short uncertain objects). On the other hand, classification is one of the most important tasks in data mining. But, to the best of our knowledge, there is no method to learn Naïve Bayesian classifier from uncertain objects. So the paper studies Naïve Bayesian classification of uncertain objects. Firstly, the paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities. Secondly, based on the theory of interval probability, the paper defines conditional interval probabilities including the intuitive concept and the canonical concept, and the conditional independence of the intuitive concept. Further, the paper gives a formula to effectively compute the intuitive concept. Thirdly, the paper presents a Naïve Bayesian classifier with interval probability parameters which can handle both uncertain objects and certain objects. Finally, experiments with uncertain objects based on UCI data show satisfactory performances.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

scholarly journals DEVELOPING A PARALLEL CLASSIFIER FOR MINING IN BIG DATA SETS

2021 ◽  
Vol 22 (2) ◽  
pp. 119-134
Author(s):  
Ahad Shamseen ◽  
Morteza Mohammadi Zanjireh ◽  
Mahdi Bahaghighat ◽  
Qin Xin
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Main Memory ◽  
Experimental Results ◽  
Primary Data ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Vast Amount ◽  
Tree Classifier

Data mining is the extraction of information and its roles from a vast amount of data. This topic is one of the most important topics these days. Nowadays, massive amounts of data are generated and stored each day. This data has useful information in different fields that attract programmers’ and engineers’ attention. One of the primary data mining classifying algorithms is the decision tree. Decision tree techniques have several advantages but also present drawbacks. One of its main drawbacks is its need to reside its data in the main memory. SPRINT is one of the decision tree builder classifiers that has proposed a fix for this problem. In this paper, our research developed a new parallel decision tree classifier by working on SPRINT results. Our experimental results show considerable improvements in terms of the runtime and memory requirements compared to the SPRINT classifier. Our proposed classifier algorithm could be implemented in serial and parallel environments and can deal with big data. ABSTRAK: Perlombongan data adalah pengekstrakan maklumat dan peranannya dari sejumlah besar data. Topik ini adalah salah satu topik yang paling penting pada masa ini. Pada masa ini, data yang banyak dihasilkan dan disimpan setiap hari. Data ini mempunyai maklumat berguna dalam pelbagai bidang yang menarik perhatian pengaturcara dan jurutera. Salah satu algoritma pengkelasan perlombongan data utama adalah pokok keputusan. Teknik pokok keputusan mempunyai beberapa kelebihan tetapi kekurangan. Salah satu kelemahan utamanya adalah keperluan menyimpan datanya dalam memori utama. SPRINT adalah salah satu pengelasan pembangun pokok keputusan yang telah mengemukakan untuk masalah ini. Dalam makalah ini, penyelidikan kami sedang mengembangkan pengkelasan pokok keputusan selari baru dengan mengusahakan hasil SPRINT. Hasil percubaan kami menunjukkan peningkatan yang besar dari segi jangka masa dan keperluan memori berbanding dengan pengelasan SPRINT. Algoritma pengklasifikasi yang dicadangkan kami dapat dilaksanakan dalam persekitaran bersiri dan selari dan dapat menangani data besar.

Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

2021 ◽  
pp. 234-259
Author(s):  
Nitika Kapoor ◽  
Parminder Singh
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Hybrid Classifier ◽  
Forest Classification ◽  
Tree Classifier

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

Prediction of warning level in aircraft accidents using data mining techniques

2014 ◽  
Vol 118 (1206) ◽  
pp. 935-952 ◽  
Author(s):  
A. B. Arockia Christopher ◽  
S. Appavu alias Balamurugan
Keyword(s):  
Data Mining ◽  
Principal Components ◽  
Information Gain ◽  
Decision Tree Classifier ◽  
Aircraft Accidents ◽  
Analysis Process ◽  
Tree Classifier ◽  
Using Data ◽  
Amount Of Knowledge ◽  
Better Than

Abstract Data mining is a data analysis process which is designed for large amounts of data. It proposes a methodology for evaluating risk and safety and describes the main issues of aircraft accidents. We have a huge amount of knowledge and data collection in aviation companies. This paper focuses on different feature selectwindion techniques applied to the datasets of airline databases to understand and clean the dataset. CFS subset evaluator, consistency subset evaluator, gain ratio feature evaluator, information gain attribute evaluator, OneR attribute evaluator, principal components attribute transformer, ReliefF attribute evaluatoboundar and symmetrical uncertainty attribute evaluator are used in this study in order to reduce the number of initial attributes. The classification algorithms, such as DT, KNN, SVM, NN and NB, are used to predict the warning level of the component as the class attribute. We have explored the use of different classification techniques on aviation components data. For this purpose Weka software tools are used. This study also proves that the principal components attribute with decision tree classifier would perform better than other attributes and techniques on airline data. Accuracy is also very highly improved. This work may be useful for an aviation company to make better predictions. Some safety recommendations are also addressed to airline companies.

scholarly journals Applying particle swarm optimization-based decision tree classifier for wart treatment selection

2021 ◽  
Author(s):  
Junhua Hu ◽  
Xiangzhu Ou ◽  
Pei Liang ◽  
Bo Li
Keyword(s):  
Decision Tree ◽  
Particle Swarm ◽  
Classification And Regression Tree ◽  
Particle Swarm Algorithm ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Cart Algorithm ◽  
Better Than

AbstractWart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.

scholarly journals Random Forest: A Hybrid Implementation for Sarcasm Detection in Public Opinion Mining

2019 ◽  
Vol 8 (12) ◽  
pp. 5022-5025
Keyword(s):  
Decision Making ◽  
Public Opinion ◽  
Random Forest ◽  
Decision Tree ◽  
Opinion Mining ◽  
Random Forest Classifier ◽  
Decision Tree Classifier ◽  
Wrong Decision ◽  
Tree Classifier ◽  
Better Than

Modelling the sentiment with context is one of the most important part in Sentiment analysis. There are various classifiers which helps in detecting and classifying it. Detection of sentiment with consideration of sarcasm would make it more accurate. But detection of sarcasm in people review is a challenging task and it may lead to wrong decision making or classification if not detected. This paper uses Decision Tree and Random forest classifiers and compares the performance of both. Here we consider the random forest as hybrid decision tree classifier. We propose that performance of random forest classifier is better than any other normal decision tree classifier with appropriate reasoning

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