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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



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

Website: http://www.ijstr.org

ISSN 2277-8616



Effective System For Prediction Of Heart Disease By Applying Logistic Regression

[Full Text]

 

AUTHOR(S)

Radha Mothukuri, Mallempudi Sai Satvik, Kolusu sri Balaji, Dodda Manikanta

 

KEYWORDS

c Regression, Cost Function, Regularization, Gradient Descent, Articial Intelligence (AI). c Regression, Cost Function, Regularization, Gradient Descent, Articial Intelligence (AI). c Regression, Cost Function, Regularization, Gradient Descent, Articial Intelligence (AI). Logistic regression, cost function, svm, random forest, Gradient descent

 

ABSTRACT

In the present current way of life of individuals are influencing by various medical problems, one among them is coronary illness which might be nascent from early age. Presently a day’s machine learning is turning into a typical instrument in medical services field. AI technology helps in logical philosophy for recognizing significant data. Machine Learning provides various advantages in medical industry. Identification of the extortion in medical coverage, accessibility of restorative answers for the patients at less cost. Acknowledgement of reasons for infections and ID of medical treatment techniques. It additionally helps the social insurance analysts for making proficient medical services strategies, building drug suggestion frameworks, creating wellbeing profiles of people and so forth. The prevalent objective of this paper is to recognize the nearness or nonattendance of coronary illness for a person. In the medical industry, it is exceptionally hard to find whether an individual is influenced by coronary illness or not by a doctor. It requires a cautious comprehension of patient’s information, and the distinguishing proof of those parameters which cause the ailment the entirety of this is considered as a troublesome assignment. Extra apparatuses are required for settling on the clinical choice of coronary illness. The data set for prediction of coronary illness, containing 303 cases, which have been utilized for the preparation and testing of the created framework. The consequences of this paper shows the regression technique like Logistic regression is being applied for the heart disease forecast so as to improve the framework productivity when contrasted with random forest and support vector machine(svm) algorithms.

 

REFERENCES

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