Data mining algorithms to compute mixed concepts with negative attributes: an application to breast cancer data analysis

2016 ◽  
Vol 39 (16) ◽  
pp. 4829-4845 ◽  
Author(s):  
Jose Manuel Rodríguez-Jiménez ◽  
Pablo Cordero ◽  
Manuel Enciso ◽  
Angel Mora
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Data Analysis ◽  
Breast Cancer Data ◽  
Cancer Data ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Negative Attributes

scholarly journals Cloud for Distributed Data Analysis Based on the Actor Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ivan Kholod ◽  
Ilya Petukhov ◽  
Andrey Shorov
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Sets ◽  
Distributed Data ◽  
Actor Model ◽  
Confidential Information ◽  
Data Mining Algorithms ◽  
Functional Blocks ◽  
Mining Algorithms ◽  
Distributed Data Analysis

This paper describes the construction of a Cloud for Distributed Data Analysis (CDDA) based on the actor model. The design uses an approach to map the data mining algorithms on decomposed functional blocks, which are assigned to actors. Using actors allows users to move the computation closely towards the stored data. The process does not require loading data sets into the cloud and allows users to analyze confidential information locally. The results of experiments show that the efficiency of the proposed approach outperforms established solutions.

scholarly journals Breast cancer data analysis for survivability studies and prediction

2018 ◽  
Vol 155 ◽  
pp. 199-208 ◽  
Author(s):  
Nagesh Shukla ◽  
Markus Hagenbuchner ◽  
Khin Than Win ◽  
Jack Yang
Keyword(s):  
Breast Cancer ◽  
Data Analysis ◽  
Breast Cancer Data ◽  
Cancer Data

scholarly journals Association Analysis of Obesity/Overweight and Breast Cancer Using Data Mining Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Mahsa Dehghani Soufi ◽  
Reza Ferdousi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Hierarchical Clustering ◽  
Cancer Progression ◽  
Clustering Algorithm ◽  
Breast Cancer Incidence ◽  
Research Work ◽  
Model Assessment ◽  
Cancer Data ◽  
Data Mining Algorithms

Introduction: Growing evidence has shown that some overweight factors could be implicated in tumor genesis, higher recurrence and mortality. In addition, association of various overweight factors and breast cancer has not been extensively explored. The goal of this research was to explore and evaluate the association of various overweight/obesity factors and breast cancer, based on obesity breast cancer data set.Material and Methods: Several studies show that a significantly stronger association is obvious between overweight and higher breast cancer incidence, but the role of some overweight factors such as BMI, insulin-resistance, Homeostasis Model Assessment (HOMA), Leptin, adiponectin, glucose and MCP.1 is still debatable, So for experiment of research work several clinical and biochemical overweight factors, including age, Body Mass Index (BMI), Glucose, Insulin, Homeostatic Model Assessment (HOMA), Leptin, Adiponectin, Resistin and Monocyte chemo attractant protein-1(MCP-1) were analyzed. Data mining algorithms including k-means, Apriori, Hierarchical clustering algorithm (HCM) were applied using orange version 3.22 as an open source data mining tool.Results: The Apriori algorithm generated a list of frequent item sets and some strong rules from dataset and found that insulin, HOMA and leptin are two items often simultaneously were seen for BC patients that leads to cancer progression. K-means algorithm applied and it divided samples on three clusters and its results showed that the pair of andlt;Adiponectin, MCP.1andgt;  has the highest effect on seperation of clusters. In addition HCM was carried out and classified BC patients into 1-32 clusters to So this research apply HCM algorithm. We carried out hierarchical clustering with average linkage without purning and classified BC patients into 1–32 clusters in order to identify BC patients with similar charestrictics.Conclusion: These finding provide the employed algorithms in this study can be helpful to our aim.

scholarly journals A Comparison Study of Data Mining Algorithms for blood Cancer Prediction

passer ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 174-179
Author(s):  
Noor Bahjat ◽  
Snwr Jamak
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Data Sets ◽  
Blood Cancer ◽  
Cancer Data ◽  
Data Mining Algorithms ◽  
Detection And Diagnosis ◽  
Mining Algorithms ◽  
Early Detection And Diagnosis

Cancer is a common disease that threats the life of one of every three people. This dangerous disease urgently requires early detection and diagnosis. The recent progress in data mining methods, such as classification, has proven the need for machine learning algorithms to apply to large datasets. This paper mainly aims to utilise data mining techniques to classify cancer data sets into blood cancer and non-blood cancer based on pre-defined information and post-defined information obtained after blood tests and CT scan tests. This research conducted using the WEKA data mining tool with 10-fold cross-validation to evaluate and compare different classification algorithms, extract meaningful information from the dataset and accurately identify the most suitable and predictive model. This paper depicted that the most suitable classifier with the best ability to predict the cancerous dataset is Multilayer perceptron with an accuracy of 99.3967%.

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