Predicting Chronic Kidney Disease Using Machine Learning Algorithms
Afia Farjana, Fatema Tabassum Liza, Partha Pratim Pandit, Madhab Chandra Das, Mahadi Hasan, Fariha Tabassum, Md Helal Hossen
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.