Effective System For Prediction Of Heart Disease By Applying Logistic Regression

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

[1] UCI Repository
https://archive.ics.uci.edu/ml/datasets/Heart+Disease
[2] R. Awang, S.Palaniappan- “Intelligent heart disease prediction system using data mining techniques.”
[3] Seyedamin pouriyeh, Juan Gutierrez, Hamid Arabnia-A Comprehensive Investigation and Comparison of Machine Learning Techniques in the Domain of Heart Disease.
[4] Prediction of heart disease using machine learning algorithms-Sonam Nikhar1, A.M. Karandikar2:.
[5] Evaluation of different machine learning techniques for prediction of heart diseases-dwivedi ashok kumar
[6] A review on heart diagnosis based on decision support systems: Saima Safdar1 • Saad Zafar1 • Naurin Frooq Khan • Nadeem Zafar2
[7] Prediction of heart diseaseusing hybrid technique for selecting features: Pahwa, Kanika, Ravinder Kumar
[8] Heart disease prediction using machine learning algorithms: Bommadevara, H. S. A., Sowmya, Y., & Pradeepini, G. (2019).
[9] Srinivas,V., Aditya, K., Prasanth, G., Babukarthik, R. G., Satheeshkumar, S., & Sambasivam, G. (2018). A novel approach for Heart disease prediction: Machine learning techniques.
[10] Analysis of single and data mining techniques for heart disease prediction using real time dataset: Yasin, S. A., & Prasad Rao, P. V. R. D.
[11] A survey on development of pattern evolving model for discovery of patterns in text mining using data mining techniques: Changala.R, D.Rajeswara rao.
[12] A computational intelligence method for effective diagnosis of heart disease using genetic algorithm: Siva kumar .p, D.Anand, V. Uday kumar, D. Bhattacharya
[13] K. SaiKrishnaSree, Narasing Rao.M.R: Diagnosis of heart disease using neural networks-comparative study of Bayesian regularization with multiple regression model.
[14] Chapman, P., Clinton, J., Kerber, R. Khabeza, T., Reinartz, T., Shearer, C.,Wirth, R.SPSS, 1-78, 2000:CRISP-DM 1.0: Step by step data mining guide.
[15] Heart disease using machine learning algorithms: Himanshu Sharma, M A Rizvi.
[16] K. Chandana, Y. Prashanth, J. Prabhudas: decision support system for predicting diabetic retinopathy using neural networks.
[17] Performance Prediction of Chronic Kidney Disease using various Data Mining Techniques,by Radha Mothukuri.International Journal of Advanced in Management,Technology and Engineering Sciences,2018,7(12),2249-7455.