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An Algorithm for Predictive Data Mining Approach in Medical Diagnosis


Affiliations
1 Department of CSE, TIT College, Bhopal, India
 

The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.

Keywords

Data Mining, Clinical Decision Support System, Disease Prediction, Classification, SVM, RF.
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Abstract Views: 328

PDF Views: 161




  • An Algorithm for Predictive Data Mining Approach in Medical Diagnosis

Abstract Views: 328  |  PDF Views: 161

Authors

Shakuntala Jatav
Department of CSE, TIT College, Bhopal, India
Vivek Sharma
Department of CSE, TIT College, Bhopal, India

Abstract


The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.

Keywords


Data Mining, Clinical Decision Support System, Disease Prediction, Classification, SVM, RF.

References