The Prediction of Diabetes in Pima Indian Women Mellitus Based on XGBOOST Ensemble Modeling Using Data Science

EasyChair Preprint 2864

Abstract

Healthcare systems provide personalized services in wide spread domains to help patients in fitting themselves into their normal activities of life. This study is focused on the prediction of diabetes in pima Indian women mellitus based on XGBOOST. Types of patients based on their personal and clinical information using a boosting ensemble technique that internally uses random committee classifier. These boosting algorithms always work well in data science competitions like Kaggle, AV Hackathon, Crowd Analytics. These are the most preferred machine learning algorithms today. To evaluate the technique, a real set of data containing 100 records is used. The prediction accuracy obtained is 81.0% based on experiments performed in Weka with 10-fold cross validation.

Keyphrases: Diabetes Mellitus, Gradient Boosting, Medical Data Mining, XGBoost, machine learning

@booklet{EasyChair:2864,
  author    = {Dasari Bhulakshmi and Glory Gandhi},
  title     = {The Prediction of Diabetes in Pima Indian Women Mellitus Based on XGBOOST Ensemble Modeling Using Data Science},
  howpublished = {EasyChair Preprint 2864},
  year      = {EasyChair, 2020}}