cancer prediction using data mining techniques
Predicting breast cancer survivability using data mining techniques
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In this paper we present an analysis of the prediction of survivability rate of breast cancer patients using data mining techniques . The data used is the SEER Public- Use Data . The preprocessed data set consists of 151,886 records, which have all the available 16 fields
Diagnosis of lung cancer prediction system using data mining classification techniques
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Cancer is the most important cause of death for both men and women. The early detection of cancer can be helpful in curing the disease completely. So the requirement of techniques to detect the occurrence of cancer nodule in early stage is increasing. A disease that is
Data mining classification techniques applied for breast cancer diagnosis and prognosis
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cancer is the most common cancer disease among women, excluding non-melanoma skin cancers group that is non- cancerous or a malignant group that is cancerous and generate Jose, A Data Mining approach for detection of high-risk Breast Cancer groups, Advances in
An analysis of heart disease prediction using different data mining techniques
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Trees, however, appears to be most effective in case of predicting patients with N.Ch.SNIyengar, Effective Analysis and Diagnosis of Lung Cancer Using Fuzzy Rules [13] Shantakumar B.Patil, YSKumaraswamy, Intelligent and Effective Heart Attack Prediction System Using
Disease prediction in data mining technique a survey
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recognized fewer than 3% of cancers and 10% recognized about 25% of the casesArtificial Neural Networks Applied to Survival Prediction in Breast [10] Delen Dursun Walker Glenn and Kadam AmitPredicting breast cancer survivability: a
An Efficient Prediction of Breast Cancer Data using Data Mining Techniques
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Breast cancer is one of the major causes of death in women when compared to all other cancers . Breast cancer has become the most hazardous types of cancer among women in the world. Early detection of breast cancer is essential in reducing life losses. This paper
Using three machine learning techniques for predicting breast cancer recurrence
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In order to predict the 2-year recurrence rate of breast cancer , we used ICBC used artificial neural networks, decision trees and logistic regression to develop prediction models for breast cancer survival by analyzing a large dataset, the SEER cancer incidence database [6
Review on heart disease prediction system using data mining techniques
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Class test, Seminar and Assignment marks were collected from the students management system, to predict the performance A Prototype of Cancer /Heart Disease Prediction Model Using Data
A survey on Data Mining approaches for Healthcare
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Data Mining are also used for both analysis and prediction of various diseases used classification tree approach to predict the cost of healthcare by using the dataset of 3 years collected detect the recurrence of breast cancer with the help of clustering technique
Early detection and prevention of cancer using data mining techniques
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levels of education whereas an inverse association has been found for the incidence of cancers of the A path is traced from root to leaf node, which holds the class prediction for that most significantly related to specific cancer types and are helpful in predicting the cancer and its
Mining Big Data : Breast Cancer Prediction using DT-SVM Hybrid Model
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is one of the major causes of death in women when compared to all other cancers An Efficient Prediction of Breast, Predicting metastasis in breast cancer : comparing a
Feature selection: An ever evolving frontier in data mining
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5. For a cancerous sample, either gene A or gene B has abnormal expression comparison of univariate and multivariate gene selection techniques for classification of cancer datasets A comparison of feature selection methods for the detection of breast cancers in mammograms
Performance analysis of various data mining classification techniques on healthcare data
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Jong Pill Choi et al.[22] compared the performance of an Artificial Neural Network, a Bayesian Network and a Hybrid Network used to predict breast cancer They also presented an analysis of the prediction of survivability rate of breast cancer patients using above
Prediction system for heart disease using Naïve Bayes
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discover and extract hidden knowledge associated with diseases (heart attack, cancer and diabetes VII. CONCLUSION Decision Support in Heart Disease Prediction System is developed using Naive Bayesian This is the most effective model to predict patients with heart disease
Predicting cancer survivability using Classification algorithms
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algorithms have a wide range of applications like fraud detection, churn prediction , artificial intelligence data explored in this research was obtained from the Oxford Cancer Survival Dataset Patients with highly developed cancers of the stomach, bronchus, colon, ovary or breast
Application of data mining methods and techniques for diabetes diagnosis
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problem is to diagnose breast cancer based on nine attributes of Wisconsin breast cancer dataset 9] The main objective of their research paper is to predict the chances are applying Naïve Bayes data mining classifier technique which produces an optimal prediction model using
Ensemble decision tree classifier for breast cancer data
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12] experimented on breast cancer data using C5 algorithm with bagging to predict breast cancer survivability Tan ACs [18] used C4.5 decision tree, bagged decision tree on seven publicly available cancerous micro array data , and compared the prediction performance of
Performance analysis of classification data mining techniques over heart disease database
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predictors that increase the length of hospital stay , Given patient records on cancer , should treatment record is provide, the CAR rule from the rule base is used to predict the class DMX) query language was used for model creation, model training, model prediction and model
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