DIABETES PREDICTION USING DECISION TREE AND XGBOOST ALGORITHM

WIRYASEPUTRA, MICHAEL (2023) DIABETES PREDICTION USING DECISION TREE AND XGBOOST ALGORITHM. Other thesis, Universitas Katholik Soegijapranata Semarang.

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Abstract

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes is a disease caused due to the increase level of blood glucose. This high blood glucose produces the symptoms of frequent urination, increased thirst, and increased hunger. Diabetes is a one of the leading cause of blindness, kidney failure, amputations, heart failure and stroke. First thing we have to do to predict the diabetes, we need to chose the good dataset for machine learning modelling. In tis case we will use pima indian diabetes as dataset. After that, test the dataset using Decision Tree and XGBoost algorithm. The result of this project is to determine how accurate the system is in processing datasets, as well as comparing algorithms between Decision Tree with XGBoost

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: mr AM. Pudja Adjie Sudoso
Date Deposited: 10 Apr 2023 01:06
Last Modified: 10 Apr 2023 01:06
URI: http://repository.unika.ac.id/id/eprint/31410

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