Prediction of Heart Disease Using Classification Based Data Mining Techniques

Joshi, Sujata; Nair, Mydhili K.

doi:10.1007/978-81-322-2208-8_46

Cited by 31 publications

(20 citation statements)

References 10 publications

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“…Weka is a collection of machine learning algorithm for pre-processing, classification, regression, clustering, association rules, and visualization. Because Weka belongs to open source software and several studies have applied to analyze, we used the classification (logistic regression, decision tree and artificial neural network) and clustering (K-means) methods in Weka to analyze our dataset [16][17][18].…”

Section: Methodologiesmentioning

confidence: 99%

The Application of Data Mining Techniques to Oral Cancer Prognosis

et al. 2015

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This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

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Section: Methodologiesmentioning

confidence: 99%

The Application of Data Mining Techniques to Oral Cancer Prognosis

et al. 2015

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“…Data mining methods can be implemented for predicting the results of the domain. Hence prediction plays a very important role [4].…”

Section: Literature Surveymentioning

confidence: 99%

“…The results show the short but accurate outcomes by applying classification which is used for the newly added patients compared to the already added ones. A model based on Combination of Naïve Bayes Classifier and K-Nearest Neighbor is proposed in [3,4,11]. Adding two more attributes helps in defining the clear result.…”

Section: Karan Khayani Sanchit Arora I Mala Serenementioning

confidence: 99%

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Heart Diseases Forecast Using Data Mining Techniques and Tools

Khayani,

Arora,

Serene I*

2019

IJITEE

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Data Mining have always been a field and combination of both computer science and statistical knowledge. From the beginning it is used to ascertain designs, patterns and arrangements which are formed in the information pool. The motive of the data mining development is to produce useful information from the pool of raw data and convert it into useful information which can be used for future arrangements. The tools which are used in data mining are helpful in predicting the future trends and predictions across the market, which also help in decision making and building the knowledge to make decisions. The “Healthcare Industry” is generally information rich. It has been collecting data to improve the continuing problems and help to identify the solutions for that problems. Data mining techniques can be used to predict heart conditions from the voluminous and complex data which are kept by the hospitals for decision making which are difficult to analyze by outmoded methods. Unfortunately, outmoded methods are less accurate in discovering hidden information from effective decision making. Data mining helps in altering the huge amount of data into knowledge driven which takes, as compared to others, less time and effort for the prediction and with greater accuracy. Our effort is to apply different data mining techniques that are used to solve the problem of biased forecasts and decision making and help in calculating the results with more accuracy.

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“…This data may be in the form of free text, structured as in databases or in the form of images. [3] This data may be used to extract meaning information which may be used for decision making. Data mining (sometimes called data or knowledge discovery) is the process of analyzing and summarizing the data from different perspective & converting these data into valuable information; it plays an important role in the intelligent medical system.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine Algorithm Based On Feature Selection For The Heart Disease Diagnosis System

Sareen¹

2018

IJRASET

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Current medicine produces a great deal of information deposited in the medical database. Extracting useful knowledge and making scientific decision for diagnosis and treatment of disease from the database increasingly becomes necessary. Data mining in medicine can deal with this problem. It can also recover the management level of hospital information and sponsor the development of tele-medicine and community medicine. The medical field is primarily directed at patient care activity and only secondarily as a research resource. The only justification for collecting medical data is to benefit the individual patient. In data mining there are numerous ways, methods to predict any illness and different researches are still going on. Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science. In the medical past it is observed that the un-structured data as mixed data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making a diagnosis of a heart patient. Various techniques in Data Mining have been applied to predict the heart disease patients. But, the indecision in the data was not detached with the systems accessible in data mining and implemented by various authors. To remove uncertainty of unstructured data, an attempt was made by introducing selective in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty and selection data was used to predict the heart disease patients.. Further, an attempt was made to classify the patients based on the attributes collected from the medical field. The maximum distance calculates the based on K-means, genetic and support vector machine classifier was designed to classify the training and testing data belonging to different classes. It was found that genetic and support vector machine classifier suits well as compared with other classifiers of parametric techniques.

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Prediction of Heart Disease Using Classification Based Data Mining Techniques

Cited by 31 publications

References 10 publications

The Application of Data Mining Techniques to Oral Cancer Prognosis

The Application of Data Mining Techniques to Oral Cancer Prognosis

Heart Diseases Forecast Using Data Mining Techniques and Tools

Support Vector Machine Algorithm Based On Feature Selection For The Heart Disease Diagnosis System

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