Implementation and Analysis of the Performance of EDTA (Enhanced Decision Tree Data Mining Algorithm) for diagnosis of Angioplasty and Stents for Heart Disease Treatment

2018 ◽  
Vol 6 (4) ◽  
pp. 541-543
Author(s):  
Amarjeet Kaur ◽  
◽  
◽  
◽  
Ashok Jetawat2 ◽  
...  
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Data Mining Algorithm ◽  
Disease Treatment ◽  
Mining Algorithm ◽  
Tree Data

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

scholarly journals Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

2021 ◽  
Vol 22 (1) ◽  
pp. 476
Author(s):  
Rini Sovia ◽  
Abulwafa Muhammad ◽  
Syafri Arlis ◽  
Guslendra Guslendra ◽  
Sarjon Defit
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Medicinal Product ◽  
Pharmaceutical Products ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
Great Demand ◽  
Mining Algorithm ◽  
Using Data

<p>This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 Data Mining Algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the C45 algorithm produces a number of rules that can identify the inheritance of a type of medicinal product. C45 algorithm is able to produce 20 types of categories that will be labeled goals based on the number of pharmaceutical products, since it can be concluded that C45 successfully defines 55% of the existing objective categories.</p>

scholarly journals Decision Tree: A Predictive Modeling Tool Used in Cloud Trust Prediction

2019 ◽  
Vol 8 (6) ◽  
pp. 4779-4783
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Modeling ◽  
Cloud Provider ◽  
Data Mining Algorithm ◽  
Trust Value ◽  
The Public ◽  
Mining Algorithm ◽  
Current Scenario ◽  
Reliable Basis

Trust is one of the important challenges faced by the cloud industry. Ever increasing data theft cases are contributing in worsening this issue. Regarding trust, author has a perception that this challenge can be handled to some extend if consumer can evaluate “Trust Value “ of the provider or can predict the same on some reliable basis. Current research is using predictive modeling for predicting trustworthiness of cloud provider. This paper is an attempt to utilize the data mining algorithm for predictive modeling. Decision Tree, a supervised data mining algorithm has been used in the current work for making predictions. Certification attainment criteria as prime basis for trust evaluation. In current scenario, data mining algorithm will classify providers in category of low, medium and high category of trust on the basis of information displayed on the public domain

scholarly journals Penerapan Decision Tree Data Mining Pada Produksi Kelapa Sawit PT Hindoli Di Sungai Lilin Kabupaten Musi Banyuasin

2020 ◽  
Vol 1 (2) ◽  
pp. 84-99
Author(s):  
Atika Kurnia ◽  
Ahmad Haidar Mirza ◽  
Andri Andri
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Palm Oil ◽  
Oil Palm ◽  
Production Data ◽  
Data Mining Algorithm ◽  
Oil Processing ◽  
Tree Data ◽  
Decision Tree Method ◽  
High Quality Seed

Data mining is an interesting pattern extraction of large amounts of data. PT Hindoli itself has a decision support information system that applies the c4.5 data mining algorithm. Given the large amount of data available, data mining estimates that palm oil production for a month is from production data. As one of the companies engaged in processing palm oil and producing palm oil, palm oil, and high-quality seed oil, which are grown by farmers into materials that can be sold and will be distributed to production data. The method used is the decision tree method to explore data, find hidden relationships between a number of prospective variables, among others, the number of producing oil palm based on the year, production, competition, and price, resulting in data accumulation or data grouping every month. Input with the target variable is expected to help PT Hindoli in monitoring palm oil processing.

scholarly journals Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test

2021 ◽  
Vol 28 (5) ◽  
pp. 118-129
Author(s):  
Alabi Waheed Banjoko ◽  
◽  
Kawthar Opeyemi Abdulazeez ◽  
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Cross Validation ◽  
Classification Method ◽  
Support Vector ◽  
Data Mining Algorithm ◽  
Machine Method ◽  
Mining Algorithm ◽  
Splitting Ratio

Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.

scholarly journals Data mining algorithm for development of a predictive model for mitigating loan risk in Nigerian banks

2021 ◽  
Vol 25 (9) ◽  
pp. 1613-1616
Author(s):  
O.B. Alaba ◽  
E.O. Taiwo ◽  
O.A. Abass
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Predictive Model ◽  
Risk Model ◽  
Data Mining Algorithm ◽  
Tree Model ◽  
Mining Algorithm ◽  
Loan Risk ◽  
Structured Questionnaire ◽  
J48 Decision Tree

The focus of this paper is on the development of data mining algorithm for developing of predictive loan risk model for Nigerian banks. The model classifies and predicts the risk involved in granting loans to customers as either good or bad loan by collecting data based on J48 decision tree, BayesNet and Naïve Bayes algorithms for a period of ten (10) years (2010 2019) from using structured questionnaire. The formulation and simulation of the predictive model were carried out using Waikato Environment for Knowledge Analysis (WEKA) software. The performance of the three algorithms for predicting loan risk was done based on accuracy and error rate metrics. The study revealed that J48 decision tree model is the most efficient of all the three models.

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