Decision System for the Selection of the Best Therapeutic Protocol for Breast Cancer Based on Advanced Data-Mining: A Survey

2019 ◽  
pp. 120-128 ◽  
Author(s):  
Sarah Khrouch ◽  
Mostafa Ezziyyani ◽  
Mohammed Ezziyyani
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Decision System ◽  
Therapeutic Protocol ◽  
Selection Of

scholarly journals An Integrated Strategy for Data Mining Based on Identifying Important and Contradicting Variables for Breast Cancer Recurrence Research

2020 ◽  
Vol 8 (6) ◽  
pp. 1096-1106
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Hybrid Model ◽  
Medical Science ◽  
Cancer Recurrence ◽  
Integrated Strategy ◽  
Selection Of Variables ◽  
Single Method ◽  
Selection Of

Cancer leads to most deaths worldwide, and breast cancer isa leading disease that causes death among women. This disease is unique in the way that once treated, can recur in some cases. Individuals are unable to identify their condition before it becomes dangerous. Extracting significant predictive features of breast cancer is an important and risky job for further study. Researchers have applied data mining techniques in medical science. Several authors suggest that a single method doesn't resolve issues in diagnosing problems, and a hybrid model is desirable. In this paper, the authors propose an integrated approachto avoid Type 1 and 2 errors in predicting recurrence. They identify important and contradicting variables and consider them for inclusion and exclusion, respectively, to revise the dataset. The evaluation of findings of crucial methods, using original and revised datasets, widens the choice of identifying the technique with higher accuracy. The results show that the accuracy improves with the selection of variables restricted to the ones identified as relatively significant, and the dataset revised after elimination of contradicting variables.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

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