scholarly journals Sistem Cerdas untuk Mendeteksi Dini Penyakit Jantung Dengan Decision Tree

Jurnal INFORM ◽  
2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Slamet Kacung
Keyword(s):  
Heart Disease ◽  
Early Detection ◽  
Decision Tree ◽  
Heart Attack ◽  
Intelligent System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
The Many ◽  
Heart Foundation

Abstract - Heart attack is the deadliest disease in the world including Indonesia. According to the report the heart Foundation Indonesia showed that the death toll reached more than 27 of 100 people due to heart disease. Early detection of heart disease is very needed considering the many people who suffer from heart disease on average already advanced stage. Intelligent system of early detection of heart disease is a method to know the symptoms that need to be alerted immediately so that heart disease could be known as early as possible. The methods used in this study using Decision Tree Classifier, the datasheet used are taken from the UCI Machine Learning Repository consisting of thirteen 270 instance, attribute input and 1 target attribute.The results of this research will result in a decision tree that can help the community and or used as a reference for a doctor in diagnosing early heart disease. The second is this research can also predict a person can be diagnosed with heart disease or not by giving the input a few symptoms that are already established, the research results cannot replace an existing heart examination but at least it can help society in General nor the doctor.

scholarly journals New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques

2021 ◽  
Author(s):  
Anwar Yahya Ebrahim ◽  
Hoshang Kolivand
Keyword(s):  
Principal Component Analysis ◽  
Decision Tree ◽  
Discrete Cosine Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Extraction System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
Handwritten Recognition

The authentication of writers, handwritten autograph is widely realized throughout the world, the thorough check of the autograph is important before going to the outcome about the signer. The Arabic autograph has unique characteristics; it includes lines, and overlapping. It will be more difficult to realize higher achievement accuracy. This project attention the above difficulty by achieved selected best characteristics of Arabic autograph authentication, characterized by the number of attributes representing for each autograph. Where the objective is to differentiate if an obtain autograph is genuine, or a forgery. The planned method is based on Discrete Cosine Transform (DCT) to extract feature, then Spars Principal Component Analysis (SPCA) to selection significant attributes for Arabic autograph handwritten recognition to aid the authentication step. Finally, decision tree classifier was achieved for signature authentication. The suggested method DCT with SPCA achieves good outcomes for Arabic autograph dataset when we have verified on various techniques.

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

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).

scholarly journals Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

2020 ◽  
Vol 10 (22) ◽  
pp. 8137
Author(s):  
Sushruta Mishra ◽  
Pradeep Kumar Mallick ◽  
Hrudaya Kumar Tripathy ◽  
Akash Kumar Bhoi ◽  
Alfonso González-Briones
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Heart Disease ◽  
Chronic Disease ◽  
Decision Tree ◽  
Classification Performance ◽  
Decision Tree Classifier ◽  
Accuracy Rate ◽  
Filter Methods ◽  
Tree Classifier

There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.

scholarly journals SISTEM DETEKSI DINI UNTUK MENINGKATKAN PERFORMANCE KELULUSAN MAHASISWA DENGAN ID3

2018 ◽  
Author(s):  
Slamet Kacung ◽  
Budi Santoso
Keyword(s):  
Higher Education ◽  
Early Detection ◽  
Decision Tree ◽  
Student Performance ◽  
Detection System ◽  
Academic Programs ◽  
Decision Tree Classifier ◽  
Study Program ◽  
Tree Classifier

The performance of academic programs higher education is measured by the number of graduates are produced by each study program as reflected in the standard III accreditation form in point 3.1.1 and 3.1.4. The study program is required to have a good performance is marked by the increasing number of graduates in proportion to the number of students received so that ratio lecturers with students can be maintained. The more students who are accepted in college if they are not comparable with the number of graduates in each year will have an impact on the quality of learning. The result of this graduation becomes the evaluation material of the study program which will be the input of the study program and the Academic Advisors (DPAM) in order to provide treatment to the problem students so that they can improve the performance of the graduates. DPAM has a very important role in the progress of the learning process of students Guide, but with the amount of guidance that is increasingly causing students to be misdirected and in the end the student performance becomes bad, for that need an early detection system to improve the performance of graduates based on the results of the recommendation from the decision tree classifier. this method can generate a decision tree and give recommendations to students problems with accuracy.

scholarly journals Heart Disease Prediction Method using Hybrid Classifier

2019 ◽  
Vol 8 (9S4) ◽  
pp. 57-61
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Research Work ◽  
Random Forest Classifier ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Hybrid Classifier ◽  
Tree Classifier

The data mining is the approach which can extract useful information from the data. The following research work that has been described is related to the heart disease prediction. The prediction analysis is the approach which can predict future possibilities based on the current information. For the heart disease prediction the classifier that is designed in this research work is hybrid classifier. 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 paper, 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. We have proposed a hybrid model that has been implemented in python. Moreover, the results are compared with Support Vector Machine (SVM) and K-Nearest Neighbor classifier (KNN).

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.

scholarly journals Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Kaushalya Dissanayake ◽  
Md Gapar Md Johar
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Decision Tree ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Classification Algorithms ◽  
Feature Subset ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Feature Selection Techniques

Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.

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