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IJSTR >> Volume 9 - Issue 9, September 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Chronic Disease Detection Model Using Machine Learning Techniques

[Full Text]



Vishal Dineshkumar Soni



SVM, Machine learning, Disease prediction, Medicine, and Accuracy



Now-a-days, people face various diseases due to the environmental condition and their living habits. So, the prediction of disease at earlier stage becomes important task. But the accurate prediction on the basis of symptoms becomes too difficult for doctor. The correct prediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has large amount of data growth per year. Due to increase amount of data growth in medical and healthcare field the accurate analysis on medical data which has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in the huge amount of medical data. Data mining is an essential phase in exploring information in libraries where intelligent tools are used to identify trends. Data mining is an important phase in exploring information in libraries where clever tools are used to identify trends. Breast cancer risk has been shown in India to develop 1 in 28 women using the precise classification to test the breast cancer data with a total of 569 rows and 32 columns. Similararly we use Heart disease dataset and Lung cancer dataset , In order to build reliable prediction models for these chronic dieseases using data mining techniques, we are evaluating data accessible from the UCI deep learning data collection in Wisconsin. “In this experiment, we compare four results of disease classification techniques with genetic clustering and comparison of the results show that sequential minimal optimization (SMO) has a higher accuracy, i.e. 99.61 percent.”



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