DIABETICS PREDICTION SYSTEM USING MACHINE LEARNING

DIABETICS PREDICTION SYSTEM USING MACHINE LEARNING

Ashwini Chaugule1, Shiwani Patil2,  Janhavi Sakate3 , Shivam Ingle4, Dhiraj Nangare5 ,

Prof. Swati Killikat6

1,2,3,4,5Students of department of Computer Science & Engineering.

6Professor of department of Computer Science & Engineering.

D Y Patil college of  Engineering & Technology Institute, kolhapur-416008

ABSTRACT

Diabetes is a serious disease that affects many people.

Age, obesity, lack of exercise, type 2 diabetes, lifestyle, poor nutrition, high blood pressure are many factors that can lead to diabetes. People with diabetes are at high risk for heart disease, eye problems, nerve damage and more.

Big data analysis plays an important role in healthcare. Medical facilities are filled with large amounts of data. Using big data analytics, big data can be examined, encapsulated data and hidden patterns can be found, thus extracting insights from the data and predicting results accordingly. In the current system, classification and prediction accuracy is not up to standard. In this article, We also proposed a theory in the form of a holistic solution to the problem of diabetes for better distribution; This theory includes several other factors that cause diabetes; BMI, age, insulin and More. In addition, it is aimed to increase the classification accuracy with higher performance by using the diabetes test model.

Keywords:

Naïve Bayes Classifier, Decision Tree Classifier, Logistic Regression, SK-learn, Pandas, Matplotlib