학술논문

Predicting Chronic Kidney Disease Using Machine Learning Algorithms
Document Type
Conference
Source
2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) Computing and Communication Workshop and Conference (CCWC), 2023 IEEE 13th Annual. :1267-1271 Mar, 2023
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Schedules
Machine learning algorithms
Sociology
Static VAr compensators
Predictive models
Chronic kidney disease
Kidney disease
Machine Learning Technique
Kidney disease prediction
classification algorithms
LighGBM
Language
Abstract
In the modern era, everyone tries to be aware of their health, but because of their workload and hectic schedules, they only pay attention to it when certain symptoms appear. However, because CKD (Chronic Kidney disease) is a disease with no symptoms or, in some cases, no symptoms at all, it is difficult to predict, detect, and prevent such a disease, which could result in long-term health damage. However, machine learning (ML) offers hope in this situation because it excels at prediction and analysis. In this paper, we proposed nine ML approaches, such as K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), Naïve Bayes, Extra tree classifiers, AdaBoost, Xgboost, and LightGBM. These predictive models are built using a dataset on chronic kidney disease with 14 attributes and 400 records to choose the best classifier for predicting chronic kidney illness. The dataset was gathered via Kaggle.com. Additionally, this study has compared how well these model's function. With the LightGBM model, we could predict kidney illness more accurately than ever before, with a 99.00% accuracy level.