The fundamental aim underlying the development of the kidney disease prediction model is to establish a resilient and precise tool for the early detection and risk evaluation of kidney ailments. Through the utilization of machine learning algorithms, the model endeavors to scrutinize an array of medical parameters, facilitating prompt prognostications and empowering proactive healthcare interventions. Ultimately, the overarching objective resides in fortifying preventive care methodologies and fostering improved patient outcomes by preemptively identifying potential risks associated with kidney disorders.
https://www.kaggle.com/datasets/mansoordaku/ckdisease
- K-Nearest Neighbors (KNN)
- Decision Tree Classifier
- Random Forest Classifier
- Ada Boost Classifier
- Gradient Boosting Classifier
- Naïve Bayes Classifier
- Logistic Regression
[Maximum Accuracy achieved: 99% (approx) using Gradient Boosting Classifier]
https://6b22b0b1-5de3-4cb3-a03a-8e5f331465e4-00-3r2xi7lguh1de.janeway.replit.dev/