A Solution to the Cross-Selling Problem of PAKDD-2007

2011 ◽  
pp. 285-289
Author(s):  
Wei Mingjun ◽  
Chai Lei ◽  
Wei Renying ◽  
Huo Wang
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Credit Card ◽  
Consumer Finance ◽  
Data Mining Algorithms ◽  
Finance Company ◽  
Final Score ◽  
The Cross ◽  
Mining Algorithms

Our team has won the Grand Champion (Tie) of PAKDD-2007 data mining competition. The data mining task is to score credit card customers of a consumer finance company according to the likelihood that customers take up the home loans offered by the company. This report presents our solution for this business problem. TreeNet and logistic regression are the data mining algorithms used in this project. The final score is based on the cross-algorithm ensemble of two within-algorithm ensembles of TreeNet and logistic regression. Finally, some discussions from our solution are presented.

Factors Related to Pediatric Unintentional Burns: The Comparison of Logistic Regression and Data Mining Algorithms

2019 ◽  
Vol 40 (5) ◽  
pp. 606-612 ◽  
Author(s):  
Abbas Aghaei ◽  
Hamid Soori ◽  
Azra Ramezankhani ◽  
Yadollah Mehrabi
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Burn Injuries ◽  
Vulnerable Groups ◽  
Support Vector ◽  
Control Group ◽  
Frequency Matching ◽  
Data Mining Algorithms ◽  
Pediatric Burns ◽  
Mining Algorithms

Abstract Burn injuries are one of the traumas seen in all parts of the world and children are usually one of the vulnerable groups. The aim of this study was to determine the factors related to unintentional burns in children, using data mining algorithms. In this hospital-based case–control study conducted in Kermanshah province, Iran, data were collected over a period of 15 months. Children under the age of 15 years old who were referred to the burn ward of Imam Khomeini Hospital, the only burn referral in Kermanshah province, were included as cases. For the control group, children who were admitted to Dr. Mohammad Kermanshahi Hospital, the only specialist and subspecialist pediatric center in this province, were included. Frequency matching was performed for age and sex. Support vector machine, artificial neural network (ANN), random forest, and logistic regression were employed to determine the factors related to burns in children. The mean age of children with burn injuries was 4.29 ± 3.51 years and 58% of them were boys. The ANN algorithm had better performance than other algorithms. Body mass index (BMI), socioeconomic status, hours without a watchful, mother’s age, mother’s education, household size, father’s job, father’s age, having more than one watchful, and petroleum storage were the most important factors related to pediatric burns. The majority of the burn-related variables were related to individuals’ social welfare status and their environments. Lessening the effects of these factors could reduce the incidence of pediatric burns.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms
Sign in / Sign up

Export Citation Format

Share Document