scholarly journals Identification of Malignant Mesothelioma Risk Factors through Association Rule Mining

2019 ◽  
Author(s):  
Talha Mahboob Alam
Keyword(s):  
Risk Factors ◽  
Association Rules ◽  
Malignant Mesothelioma ◽  
Exposure Duration ◽  
Asbestos Exposure ◽  
Rule Mining ◽  
Data Set ◽  
Duration Of Symptoms ◽  
Factors Associated ◽  
Irrelevant Attributes

Malignant mesothelioma is a rare proliferative cancer that develops in the thin layer of tissues surrounding the lungs. Malignant mesothelioma is associated with an extremely poor prognosis and the majority of patients do not show symptoms. The epidemiology of mesothelioma is important for the identification of disease. The primary aim of this study is to explore the risk factors associated with mesothelioma. The dataset consists of healthy and mesothelioma patients but only mesothelioma patients were selected for the identification of symptoms. The raw data set has been pre-processed and then the Apriori method was utilized for association rules with various configurations. The pre-processing task involved the removal of duplicated and irrelevant attributes, balanced the dataset, numerical to the nominal conversion of attributes in the dataset and creating the association rules in the dataset. Strong associations of disease’s factors; asbestos exposure, duration of asbestos exposure, duration of symptoms, erythrocyte sedimentation rate and Pleural to serum LDH ratio determined via Apriori algorithm. The identification of risk factors associated with mesothelioma may prevent patients from going into the high danger of the disease. This will also help to control the comorbidities associated with mesothelioma which are cardiovascular diseases, cancer-related emotional distress, diabetes, anemia, and hypothyroidism.

Risk Factors Associated with VOD with RIC and MAC Hematopoietic Stem Cell Transplant and the Early Estimation of VOD

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4315-4315
Author(s):  
Shoichi Nagakura ◽  
Tetsuyuki Kiyokawa ◽  
Michihiro Hidaka ◽  
Takahiro Yano ◽  
Kazutaka Sunami ◽  
...  
Keyword(s):  
Risk Factors ◽  
Goodness Of Fit ◽  
Time Course ◽  
Laboratory Data ◽  
P Value ◽  
Cell Transplant ◽  
Data Set ◽  
Hematopoietic Stem ◽  
Post Transplant ◽  
Factors Associated

Abstract BACKGROUND: Despite recent increase of reduced intensity conditioning (RIC) transplantation, mortality rates after RIC and myeloabrative conditioning (MAC) HSCT remain high and hepatic veno-occlusive disease (VOD) cannot accurately predicted. OBJECTIVE: To determine the value of risk factors associated with the development of VOD after allergenic HSCT with RIC and MAC. Estimating VOD based on clinical factors may further improve results of allogenic HSCT. PATIENTS AND METHODS: A retrospective review of 415 consecutive allogenic HSCT was performed with attention to VOD, pre-transplant factors and laboratory data in five hematopoietic cell transplantation centers between 2000 and 2005. Patients underwent transplantation with MAC (n=247) or RIC (n=168). Main outcomes and risk factors were analyzed in multivariable analyses (a logistic regression model) with RIC and MAC. Three kind of laboratory data set, pre-transplant (day −10), post-transplant (day 20) and differences from pre-transplant to post-transplantation were analyzed. RESULTS: VOD occurred in 65 of 415(15.7%) transplant recipients; 40 of 247(16.1%) with MAC and 25 of 168(14.9%) with RIC. Multivariate analyses identified risk factors with the development of VOD with MAC (albumin level, creatinine level) and with RIC (HCT-CI, number of prior chemotherapy regimen, ALT) in pre-transplant laboratory data set. The risk factors of VOD were identified in post-transplant and differences (Table). The Akaike’s information criterion (AIC) of risk factors with differences was better than with the post-transplant. CONCLUSION: Our results provided risk factors of VOD with MAC and RIC. The estimation of VOD before transplantation may be useful for the selection of conditioning regimens. Differences of laboratory data with the time course of transplant may be useful for the early diagnosis of VOD. MAC Pre-transpant data Post-transplant data Differences data OR P-Value OR P-Value OR P-Value Age - - 0.945 0.0090 - - Alb 0.290 0.0125 - - - - Cr 10.204 0.0307 1.786 0.0039 1.984 0.0139 TPro - - 0 358 0.0019 - - TBi I - - 1.385 0.0027 1.314 0.0037 Ara-C - - 5.000 0.0139 goodness of fit AIC 106.727 126.499 86.931 RIC Pre-transpant data Post-transplant data Differences data OR P-Value OR P-Value OR P-Value Sex - - 3.401 0.0446 - - HCTCI 3.922 0.0050 2.000 0.0123 - - ImpScore 2.000 0.0314 - - - - TPro - - 0.366 0.0091 - - TBi I - - 1.675 0.0042 2.273 0.0004 ALT 0.969 0.0432 - - - - CY - - - - 5.682 0.0447 goodness of fit AIC 61.552 91.09 52.808

Filtering Association Rules by Their Semantics and Structures

2010 ◽  
pp. 216-230
Author(s):  
Rangsipan Marukatat
Keyword(s):  
Association Rules ◽  
Association Rule Mining ◽  
Traffic Accidents ◽  
Complete Information ◽  
Simple Rule ◽  
Rule Generation ◽  
Rule Mining ◽  
Data Set ◽  
Domain Description

Association rule mining produces a large number of rules but many of them are usually redundant ones. When a data set contains infrequent items, the authors need to set the minimum support criterion very low; otherwise, these items will not be discovered. The downside is that it leads to even more redundancy. To deal with this dilemma, some proposed more efficient, and perhaps more complicated, rule generation methods. The others suggested using simple rule generation methods and rather focused on the post-pruning of the rules. This chapter follows the latter approach. The classic Apriori is employed for the rule generation. Their goal is to gain as much insight as possible about the domain. Therefore, the discovered rules are filtered by their semantics and structures. An individual rule is classified by its own semantic, or by how clear its domain description is. It can be labelled as one of the following: strongly meaningless, weakly meaningless, partially meaningful, and meaningful. In addition, multiple rules are compared. Rules with repetitive patterns are removed, while those conveying the most complete information are retained. They demonstrate an application of our techniques to a real case study, an analysis of traffic accidents in Nakorn Pathom, Thailand.

scholarly journals Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment

2017 ◽  
Vol 24 (06) ◽  
pp. 1740005 ◽  
Author(s):  
Meera Sharma ◽  
Abhishek Tandon ◽  
Madhu Kumari ◽  
V. B. Singh
Keyword(s):  
Operating System ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Clustering Algorithm ◽  
Large Data ◽  
Software Project ◽  
Rule Mining ◽  
Data Set ◽  
Bug Reports

Bug triaging is a process to decide what to do with newly coming bug reports. In this paper, we have mined association rules for the prediction of bug assignee of a newly reported bug using different bug attributes, namely, severity, priority, component and operating system. To deal with the problem of large data sets, we have taken subsets of data set by dividing the large data set using [Formula: see text]-means clustering algorithm. We have used an Apriori algorithm in MATLAB to generate association rules. We have extracted the association rules for top 5 assignees in each cluster. The proposed method has been empirically validated on 14,696 bug reports of Mozilla open source software project, namely, Seamonkey, Firefox and Bugzilla. In our approach, we observe that taking on these attributes (severity, priority, component and operating system) as antecedents, essential rules are more than redundant rules, whereas in [M. Sharma and V. B. Singh, Clustering-based association rule mining for bug assignee prediction, Int. J. Business Intell. Data Mining 11(2) (2017) 130–150.] essential rules are less than redundant rules in every cluster. The proposed method provides an improvement over the existing techniques for bug assignment problem.

scholarly journals Clinical characteristics, risk factors and outcome of severe Norovirus infection in kidney transplant patients: a case-control study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Julien Gras ◽  
Moustafa Abdel-Nabey ◽  
Axelle Dupont ◽  
Jérôme Le Goff ◽  
Jean-Michel Molina ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Rejection ◽  
Kidney Transplant ◽  
Case Control Study ◽  
De Novo ◽  
Case Control ◽  
Duration Of Symptoms ◽  
Factors Associated ◽  
Control Study

Abstract Background Human Norovirus (HuNoV) has recently been identified as a major cause of diarrhea among kidney transplant recipients (KTR). Data regarding risk factors associated with the occurrence of HuNoV infection, and its long-term impact on kidney function are lacking. Methods We conducted a retrospective case-control study including all KTR with a diagnosis of HuNoV diarrhea. Each case was matched to a single control according to age and date of transplantation, randomly selected among our KTR cohort and who did not develop HuNoV infection. Risk factors associated with HuNoV infection were identified using conditional logistic regression, and survival was estimated using Kaplan-Meier estimator. Results From January 2012 to April 2018, 72 cases of NoV diarrhea were identified among 985 new KT, leading to a prevalence of HuNoV infection of 7.3%. Median time between kidney transplantation and diagnosis was 46.5 months (Inter Quartile Range [IQR]:17.8–81.5), and the median duration of symptoms 40 days (IQR: 15–66.2). Following diagnosis, 93% of the cases had a reduction of immunosuppression. During follow-up, de novo Donor Specific Antibody (DSA) were observed in 8 (9%) cases but none of the controls (p = 0.01). Acute rejection episodes were significantly more frequent among cases (13.8% versus 4.2% in controls; p = 0,03), but there was no difference in serum creatinine level at last follow-up between the two groups (p = 0.08). Pre-transplant diabetes and lymphopenia below 1000/mm3 were identified as risks factors for HuNoV infection in multivariate analysis. Conclusion HuNoV infection is a late-onset and prolonged infection among KTR. The current management, based on the reduction of immunosuppressive treatment, is responsible for the appearance of de novo DSA and an increase in acute rejection episodes.

DISTRIBUTED MINING OF ASSOCIATION RULES BASED ON REDUCING THE SUPPORT THRESHOLD

2008 ◽  
Vol 17 (06) ◽  
pp. 1109-1129 ◽  
Author(s):  
BASILIS BOUTSINAS ◽  
COSTAS SIOTOS ◽  
ANTONIS GEROLIMATOS
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Processing Power ◽  
Support Threshold ◽  
Empirical Tests

One of the most important data mining problems is learning association rules of the form "90% of the customers that purchase product x also purchase product y". Discovering association rules from huge volumes of data requires substantial processing power. In this paper we present an efficient distributed algorithm for mining association rules that reduces the time complexity in a magnitude that renders as suitable for scaling up to very large data sets. The proposed algorithm is based on partitioning the initial data set into subsets and processing each subset in parallel. The proposed algorithm can maintain the set of association rules that are extracted when applying an association rule mining algorithm to all the data, by reducing the support threshold during processing the subsets. The above are confirmed by empirical tests that we present and which also demonstrate the utility of the method.

A Kernel Density Estimation Based Interestingness Measure for Association Rule Mining

2010 ◽  
Vol 20-23 ◽  
pp. 389-394
Author(s):  
Zhi Feng Hao ◽  
Rui Chu Cai ◽  
Tang Wu ◽  
Yi Yuan Zhou
Keyword(s):  
Density Estimation ◽  
Association Rules ◽  
Kernel Density Estimation ◽  
Association Rule ◽  
Association Rule Mining ◽  
Kernel Density ◽  
Rule Mining ◽  
Data Set ◽  
Interestingness Measures ◽  
Interestingness Measure

Association rules provide a concise statement of potentially useful information, and have been widely used in real applications. However, the usefulness of association rules highly depends on the interestingness measure which is used to select interesting rules from millions of candidates. In this study, a probability analysis of association rules is conducted, and a discrete kernel density estimation based interestingness measure is proposed accordingly. The new proposed interestingness measure makes the most of the information contained in the data set and obtains much lower falsely discovery rate than the existing interestingness measures. Experimental results show the effectiveness of the proposed interestingness measure.

scholarly journals Diffuse epithelioid malignant mesothelioma of the pleura presenting as a hydropneumothorax and vertebral body invasion

2020 ◽  
Vol 13 (1) ◽  
pp. e231987
Author(s):  
Colin Andrew Hinkamp ◽  
Shanup N Dalal ◽  
Yasmeen Butt ◽  
Alberto V Cabo Chan
Keyword(s):  
Risk Factors ◽  
Vertebral Body ◽  
Malignant Mesothelioma ◽  
Asbestos Exposure ◽  
Neoplastic Transformation ◽  
The United States ◽  
The Body ◽  
Pleural Mesothelioma ◽  
Pleural Effusions ◽  
Short Term

Malignant mesothelioma is an uncommon form of neoplastic transformation of the mesothelial cells that line the serosal surfaces of the body. It most commonly affects the pleura and is often associated with pleural effusions and pleural-based masses. The annual incidence in the United States is only 3300 cases, representing less than 0.3% of all cancers worldwide, although this is likely underestimated. We present a case of diffuse epithelioid malignant pleural mesothelioma in a patient with remote, short-term asbestos exposure complicated by recurrent left-sided hydropneumothoraces and pleural-based invasion of the T12 vertebral body, which represent two rare coexisting complications. This case illustrates the importance of maintaining a broad differential for hydropneumothorax, particularly as the risk factors may be decades removed and the degree of asbestos exposure to induce a malignant mesothelioma may be smaller than has been traditionally thought.

scholarly journals Mining Temporal Association Rules with Temporal Soft Sets

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xiaoyan Liu ◽  
Feng Feng ◽  
Qian Wang ◽  
Ronald R. Yager ◽  
Hamido Fujita ◽  
...  
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Temporal Association ◽  
Data Sets ◽  
Rule Mining ◽  
Soft Sets ◽  
Data Set ◽  
Temporal Association Rule ◽  
Temporal Aspect ◽  
Necessary And Sufficient

Traditional association rule extraction may run into some difficulties due to ignoring the temporal aspect of the collected data. Particularly, it happens in many cases that some item sets are frequent during specific time periods, although they are not frequent in the whole data set. In this study, we make an effort to enhance conventional rule mining by introducing temporal soft sets. We define temporal granulation mappings to induce granular structures for temporal transaction data. Using this notion, we define temporal soft sets and their Q -clip soft sets to establish a novel framework for mining temporal association rules. A number of useful characterizations and results are obtained, including a necessary and sufficient condition for fast identification of strong temporal association rules. By combining temporal soft sets with NegNodeset-based frequent item set mining techniques, we develop the negFIN-based soft temporal association rule mining (negFIN-STARM) method to extract strong temporal association rules. Numerical experiments are conducted on commonly used data sets to show the feasibility of our approach. Moreover, comparative analysis demonstrates that the newly proposed method achieves higher execution efficiency than three well-known approaches in the literature.

scholarly journals Extraction of Association Rule Mining using Apriori algorithm with Wolf Search Optimisation in R Programming

2019 ◽  
Vol 8 (2S7) ◽  
pp. 504-507 ◽  
Keyword(s):  
Association Rules ◽  
Hybrid Approach ◽  
Numerical Data ◽  
Rule Mining ◽  
Data Set ◽  
Regular Elements ◽  
Fitness Functions ◽  
Local Solutions ◽  
R Programming ◽  
Data Discretization

Association rules mining (ARM) is a standout amongst the most essential Data Mining Systems. Find attribute patterns as a binding rule in a data set. The discovery of these suggestion rules would result in a mutual method. Firstly, regular elements are produced and therefore the association rules are extracted. In the literature, different algorithms inspired by nature have been proposed as BCO, ACO, PSO, etc. to find interesting association rules. This article presents the performance of the ARM hybrid approach with the optimization of wolf research based on two different fitness functions. The goal is to discover the best promising rules in the data set, avoiding optimal local solutions. The implementation is done in numerical data that require data discretization as a preliminary phase and therefore the application of ARM with optimization to generate the best rules.

scholarly journals Identification of Preoperative Risk Factors Associated With the Conversion of Laparoscopic to Open Appendectomies

2013 ◽  
Vol 98 (4) ◽  
pp. 334-339 ◽  
Author(s):  
Natasha Gupta ◽  
David Machado-Aranda ◽  
Keturah Bennett ◽  
Vijay K. Mittal
Keyword(s):  
Risk Factors ◽  
Postoperative Complications ◽  
Conversion Rate ◽  
Open Technique ◽  
Preoperative Risk ◽  
Duration Of Symptoms ◽  
Female Ratio ◽  
Factors Associated ◽  
Preoperative Risk Factors ◽  
Conversion Rates

Abstract Our goals were to (1) identify risk factors associated with conversion from laparoscopic to open appendectomies and (2) establish criteria that predict the possibility of conversion to an open technique. We did a retrospective chart review of all patients who underwent laparoscopic appendectomies during a 5-year period (2004–2008). Preoperative risk factors, intraoperative findings, and postoperative complications were compared. We found that of 763 patients who had undergone laparoscopic appendectomy, 44 patients were converted to open technique (conversion rate of 5.8%). For these 44 patients, the male to female ratio was 2 to 1, and the men were older (45 versus 37 years of age, P < 0.001). Conversion rates decreased with time (8.7% in 2004 versus 3.5% in 2008). Past surgical history was insignificant. However, a duration of symptoms of >5 days as well as a white blood cell count >20,000 were found to have a direct correlation. Incidence of postoperative complications did not increase in converted patients. The conversion rate is highest in male patients above 45 years of age, with over 5 days' duration of symptoms, leukocytosis >20,000, and ruptured appendicitis on computed tomography scan. The presence of 3 to 4 of these risk factors should lower the threshold for consideration of conversion to open appendectomy.

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