Prediction of Benign and Malignant Breast Cancer Using Data Mining Techniques

Chaurasia, Vikas; Pal, Saurabh

doi:10.2139/ssrn.3139141

Cited by 86 publications

(107 citation statements)

References 5 publications

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“…Gamberger et al [7] studied that with the aid of information mining model Intelligent Heart Disease Forecasting System (IHDPS) innovation workmanship for example, decision trees, naïve Bayes and neural system. Chaurasia and Pal [8] explained that the death of the history of the largest study shows that heart disease has gradually become the world's number one killer. Death age group occurs from 25 to 69 years old about 25% due to heart disease.…”

Section: Review Of Literaturementioning

confidence: 99%

Data Mining Techniques to Construct a Model: Cardiac Diseases

Akhtar¹,

Talib²,

Kanwal³

2018

ijacsa

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Abstract-Using echocardiography flexible TransthoracicEchocardiography reported data set detecting heart disease by using mining techniques designed prediction model the data set can develop the reliability of analysis of cardiac diseases by echocardiography, using eight iterative and interactive steps consisting Knowledge Discovery in Database (KDD) methodology including from 209 patients with echocardiography to extracting the data important mode of action Transthoracic Echocardiography inspection report. This study used data from Faisalabad Institute of Cardiology study from 2012 to 2015. All models exposed the results of J48 decision tree, naïve bayes classifier and neural network that has extraordinary classification precision and predictive of heart disease cases are generally comparable. However, J48 model predictive classification accuracy shows of 80% based on the true positive rate ratio and performance slightly better. This study shows to predict heart disease cases and People can be used the results of our study to make more consistent diagnosis of cardiac disease and to help them as a support tool for cardiac disease specialists.

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Section: Review Of Literaturementioning

confidence: 99%

Data Mining Techniques to Construct a Model: Cardiac Diseases

Akhtar¹,

Talib²,

Kanwal³

2018

ijacsa

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“…Accuracy of decision tree and Bayesian classification further improves after applying genetic algorithm so that minimal number of attributes can be selected. Paper [6] used CART, ID3 and Decision Table algorithms with 10-fold cross validation. Prototype of system described by Researcher [7] can use two data mining algorithms i.e.…”

Section: Related Workmentioning

confidence: 99%

Sudden Cardiac Arrest Prediction Using Predictive Analytics

Bhatt¹,

Dubey²,

Bhatt³

2017

IJIES

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In today's modern world, Cardiovascular Disease is one of the most lethal one. Sudden Cardiac Death is the result of this heart disease that attacks a person so instantly that it hardly gives seconds to be operated and heart freezes causing death at spot which makes it more sever and complicated for hospitals and medical services. In this paper, we have given our efforts to predict the possibility of occurring of these quick and killing attacks using decision model based predictive analytics techniques, so that we can analyze and find some patterns that are common in the happenings of Sudden Cardiac Death. Unfortunately, most hospitals and medical service organization's data are rarely used in clinical research while these datasets has such a huge potential by applying for predictive analytical approach. Classification Algorithms that comes under the decision model based in order to predict the probability of Sudden Cardiac Attack on heart disease patients. From our results, we came to know that in Hungarian database as well as in Echocardiograph database, Naïve Bayes algorithm outperformed all other algorithms and showed the maximum accuracy. In order to tackle this situation, we have proposed a framework that can be implemented for emergency situations of people with such medical history and these raw datasets are further analyzed at scratch to predict the upcoming life threatening pain that might cause death.

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“…It needs proper attention and consideration of doctor about the patient. In this regard, data mining techniques are used on a large scale to avoid such kind of activities [13].…”

Section: Application Areamentioning

confidence: 99%

A Comparative Study on Usage of Data Mining Techniques in Healthcare Sector

Humayun¹,

Waqar²

2017

IJCA

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Recent advancement in the data mining technique has provided a platform to numerous applications in healthcare sector. It has become an active research area due to its large scale potential. In this survey, we have tried to present the application areas of data mining approaches specifically in the healthcare sector. The different approaches of the data mining and where they are used is also described in the paper. In this work, we have also tried to explain the different challenges faced by the data mining techniques in order to implement them. This survey also covers the pros and cons of the data mining techniques in healthcare. Data mining techniques have a tremendous future and it should be taken at its earliest because of the significant importance of the healthcare issues.

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Prediction of Benign and Malignant Breast Cancer Using Data Mining Techniques

Cited by 86 publications

References 5 publications

Data Mining Techniques to Construct a Model: Cardiac Diseases

Data Mining Techniques to Construct a Model: Cardiac Diseases

Sudden Cardiac Arrest Prediction Using Predictive Analytics

A Comparative Study on Usage of Data Mining Techniques in Healthcare Sector

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