scholarly journals Cardiovascular Disease Prediction System Using Extra Trees Classifier

2019 ◽  
Author(s):  
Rahman Shafique ◽  
Arif Mehmood ◽  
Saleem ullah ◽  
Gyu Sang Choi
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Health Care ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Support Vector ◽  
Prediction System ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Tree Classifier

Abstract Heart Disease as cardiovascular disease is the leading cause of death for both men and women. It is the major cause of morbidity and mortality in present society. Therefore, researchers are working to help health care professionals in diagnosing process by using data mining techniques. Although the health care industry is richer in the database this data is not properly mined in order to discover hidden patterns and can able to make decisions based on these patterns. The major goal of this learning refers the extraction of hidden layers by applying numerous data mining techniques that probably give remarkable results in order to ensure the presence of cardiovascular disease among peoples. Data mining classification techniques are used to discover these patterns for research in medical industry. The dataset containing 13 attributes has analyzed for prediction system. The dataset contains some commonly used medical terms like blood pressure, cholesterol level, chest pain and 11 other attributes used to predict cardiovascular disease. The most common and effective classification techniques that are used in mining process are Verdict Tree commonly known as Decision Tree, Extra Trees Classifier, Random Forest, Support Vector Machine, Naive Bays and Logistic Regression has analyzed in this paper. Diagnosing and controlling ratio of deaths from cardiovascular disease Extra classifier trees consider is the best approach. We evaluate these prediction models by using evaluation parameters which are Accuracy, Precision, Recall, and F1-score. As per our experimental results shows accuracy of Extra trees classifier, Logistic Model tree classifier, support vector machine, and naive bays classifiers are 90%, 88%, 87%, 86% respectively. So as per our experiment analysis Extra Tree classifier with highest accuracy considered best approach for predication cardiovascular disease.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

scholarly journals The Data Mining Support Vector Machine Algorithm used for Detecting and Forecasting of Crimes

2019 ◽  
Vol 9 (1) ◽  
pp. 395-401
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Mining System ◽  
Data Mining Techniques ◽  
Crime Data ◽  
Data Archives ◽  
Crime Investigation ◽  
Chicago Police

Crime rate is expanding extremely more because of destitution and joblessness. With the current crime investigation techniques, officers need to invest a great deal of energy just as labor to recognize suspects and criminals. Anyway crime investigation procedure should be quicker and dynamic. As huge amount of data is gathered during crime investigation, data mining is a methodology which can be valuable in this viewpoint. Data mining is a procedure that concentrates valuable data from enormous amount of crime data with the goal that potential suspects of the crime can be recognized productively. Quantities of data mining techniques are accessible. Utilization of specific data mining system has more prominent impact on the outcomes acquired. So the exhibition of three data mining techniques will be analyzed against test crime and criminal database and best performing algorithm will be utilized against test crime and criminal database to recognize potential suspects of the crime. Data mining is a procedure of separating information from colossal amount of data put away in databases, data stockrooms and data archives. Clustering is the way toward consolidating data objects into gatherings. Here taken the Crime dataset from Chicago police website and implemented in MATLAB utilizing Support Vector Machine algorithm.

scholarly journals Interpretation of Urban Power Consumers Behaviors to Predict Power Loss in Summer

2019 ◽  
Vol 9 (1) ◽  
pp. 563-565 ◽  
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Energy Consumption ◽  
High Temperature ◽  
Power Loss ◽  
Electricity Consumption ◽  
Sequential Machine ◽  
Support Vector ◽  
Data Mining Techniques ◽  
Electricity Theft

High Temperature In The Summer Of India. Interpretation Of Electricity Consumption Is Crucial In Summer For Urban Consumers. We Are Focus Here For Only Indian Summer Urban Customers Energy Consumption To Analysis And Predict Behavior Of Electricity Theft. Data Mining Techniques Are Employing To Analyses Indian Summer Urban Customers. Online Sequential Machine And Support Vector Machine Is Used For This Behaviors Classification And Prediction.Mainly we focus Support vector machine to classified consumers and online sequential machine is used to detect and predict consumers behaviors.

scholarly journals Prediction of active debt in the State of Pernambuco, Brazil

2020 ◽  
Vol 5 (1) ◽  
pp. 88-95
Author(s):  
Álvaro Farias Pinheiro ◽  
João Alberto Da Silva Amaral ◽  
Geraldo Torres Galindo Neto ◽  
José Nilo Martins Sampaio ◽  
Wedson Lino Soares
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
The State ◽  
Support Vector ◽  
Data Mining Techniques ◽  
Collection Process ◽  
Mining Model

Application of data mining (DM) techniques to optimize the process of collection of Active Debt (AD) of the State of Pernambuco, Brazil. We apply the following data mining techniques: Decision Tree (DT), Logistic regression (LR), Nayve bayes (NB), Support vector machine (SVM), also applied to the Random Forest technique which is considered an essemble method. We observed that the RF technique obtained better results than all the techniques of classification, reaching higher values in all metrics analyzed. We note that the creation of a data mining model to choose which debts can succeed in the collection process can bring benefits to the pernambuco government. With the application of RF technique, we obtained indexes above 85% in the evaluation of the metrics.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2014 ◽  
pp. 327-360 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2016 ◽  
pp. 923-954
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

Audit Opinion Prediction: A Comparison of Data Mining Techniques

2020 ◽  
Author(s):  
Ali Saeedi
Keyword(s):  
Data Mining ◽  
New York ◽  
Prediction Models ◽  
Stock Exchange ◽  
Support Vector ◽  
Type I ◽  
K Nearest Neighbors ◽  
Data Mining Techniques ◽  
Financial Variables ◽  
Audit Opinions

This study compares the ability of four data mining techniques in the prediction of audit opinions on companies' financial statements. The research data consists of 37,325 firm-year observations for companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2001 to 2017. The dataset consists of U.S. companies' variousfinancial and non-financial variables. This study uses Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (k-NN), and Rough Sets (RS) to develop the prediction models. While all models developed by these four techniques predict the audit opinions with relatively high accuracy, the SVM models developed by RBF kernel demonstrate the highest performance in terms of overall prediction accuracy rates and Type I and Type II errors. The results indicate that all models developed using different algorithms demonstrate their highest performance in predicting going-concern modifications, ranging from 84.2 to 100 percent.

Machine Learning Based Parametric Estimation Approach for Poll Prediction

2019 ◽  
Vol 13 ◽  
Author(s):  
Abdul Manan koli ◽  
Muqeem Ahmed
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Prediction System ◽  
Jammu And Kashmir ◽  
Learning Techniques ◽  
Election Prediction

Background: The process of election prediction started long back when common practice for election predictions were traditional methods like pundits, hereditary factor etc. However, in recent times new methods and techniques are being used for election forecasting like Data mining, Data Science, Big data, and numerous machine learning techniques. By using such computational techniques the whole process of political forecasting is changed and poll predictions are carried out through them. Method: The election prediction model is developed in Jupyter notebook web application using different supervised machine learning techniques. To obtain the optimal results, we perform the hyperparameter tuning of all the proposed classifiers. For measuring the performance of poll prediction system we used confusion matrix along with AUROC curve which depicts that this methods can be well suited for political forecasting. An important contribution of this article is to design a Prediction system which can be used for making prediction in other fields like cardiovascular disease predictions, weather forecasting etc. Results: This model is tested and trained with real-time dataset of the state Jammu and Kashmir (India). We applied features selection techniques like Random Forest, Decision Tree Classifier, Gradient boosting Classifier and Extra Gradient Boosting and obtained eight most important parameters like (Central Influence, Religion Followers, Party Wave, Party Abbreviations, Sensitive Areas, Vote Bank, Incumbent Party, and Caste Factor) for poll predictions with their mean weightages. By applying different classifier to get mean weightage of different parameters for this election prediction models, it has been observed that Party wave got maximum mean weightage of 0.82% as compared to others parameters. After obtaining the vital parameters for political forecasting, we applied various machine learning algorithms like Decision tree, Random forest, K-nearest neighbor and support vector machine for the early prediction of elections. Experimental results show that Support Vector Machine outperformed with a higher accuracy of 0.84% in contrast to others classifiers. Conclusion: In this paper, a clear overview of election prediction models, their potentials, techniques, parameters as well as limitations are outlined. We conclude this work by stating that election predictions can indeed be forecasted with significant parameters however, with caution due to the limitations which were outlined in developing nations like sensitive areas, social unrest, religion etc. This research work may be considered as the first attempt to use multiple classifier for forecasting the Assembly election results of the state Jammu and Kashmir (India).

scholarly journals MODEL KLASIFIKASI KEPUASAN MAHASISWA TEKNIK TERHADAP SARANA PEMBELAJARAN MENGGUNAKAN DATA MINING

2020 ◽  
Vol 14 (2) ◽  
pp. 112-118
Author(s):  
Ariesta Lestari ◽  
Elga Mariati ◽  
Widiatry Widiatry
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Prediction System ◽  
Data Mining Approach

Student in one of the stakeholder in a university. Therefore, student’s perception in the quality of learning facilities and infrastructures become important to ensure the university’s performance.  The Faculty of Engineering of University of Palangka Raya has not comprehensively evaluated the students’ satisfactory of the learning’s facilities. In this research, methods from data mining approach was implemented to classify whether the students satisfy or not with the quality of the learning’s facility in Engineering Faculty.  This research compared three data mining algorithm, Decision Tree C4.5, Support Vector Machine, and Naïve Bayes to obtain the best algorithm for the prediction system. 948 responses were collected, 61% of the respondent were satisfied with the quality of the learning facilities and infrastructures, while 39% of the respondents were dissatisfied. The Decision Tree c4.5 had the best performance with accuracy of 88%  and precision of 98% compared to the Naïve Bayes and support vector machine.

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