chronic kidney disease detection using machine learning | AI,ML | Best IEEE Machine Learning Project
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In this video, we delve into the world of Chronic Kidney Disease (CKD) detection using advanced machine learning techniques. We start by explaining what CKD is, its causes, impacts, and treatment options, as well as the different stages of the disease.
We then analyze a base research paper that utilized ANN, KNN, and SVM models for CKD detection. Building on this, we propose our own methodology, where we enhance the dataset by addressing imbalances through up-sampling, handle missing values, and apply advanced feature selection techniques like wrapping and LASSO, which were not covered in the base paper.
Our project takes CKD detection to the next level by building and testing 10 different machine learning models, significantly more than the three models discussed in the base paper. We're proud to report that six of our models achieve around 100% accuracy, while the remaining four deliver 98% accuracy—outperforming the base paper, which had a maximum accuracy of 99.13%. Our classification reports also show perfect scores in accuracy, recall, F1 score, and precision.
Additionally, we demonstrate how we built a user-friendly frontend using HTML, CSS, and JavaScript, and integrated it with a Flask backend to create a complete CKD detection system.
If you're interested in the full code, documentation, or need mentorship for your own project, feel free to contact us at 8088605682 or message us on WhatsApp.
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🚨 Chronic Kidney Disease Detection Using Machine Learning | Comprehensive Project Walkthrough 🚨
👋 Welcome to our channel! In this video, we take you on an in-depth journey through our Chronic Kidney Disease (CKD) detection project powered by cutting-edge Machine Learning techniques. Whether you're a student, a developer, or simply curious about AI in healthcare, this video will provide you with valuable insights and practical knowledge.
🔍 What You'll Learn in This Video:
1️⃣ Introduction to Chronic Kidney Disease (CKD)
We kick off by explaining the basics of CKD, a serious condition that affects millions worldwide. Learn about the causes, symptoms, and the different stages of CKD, as well as the potential treatments available. Understanding these fundamentals sets the stage for why early detection through machine learning is crucial.
2️⃣ Analyzing a Key Research Paper
We review a foundational research paper that used Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) to detect CKD. This paper provided us with a starting point, but we knew we could take the methodology further.
3️⃣ Our Enhanced Methodology 🚀
This is where things get exciting! We propose our own approach, which goes beyond the original paper:
Dataset Collection & Preprocessing: We gathered a comprehensive CKD dataset and tackled the challenges of data imbalance by applying up-sampling techniques—something the base paper didn't address. We also dealt with missing values, ensuring our dataset was clean and reliable.
Advanced Feature Selection: Using wrapping techniques and the LASSO method, we carefully selected the most important features for our models, refining the predictive power of our machine learning algorithms.
Building 10 Different ML Models: Unlike the base paper, which only used three models, we experimented with ten different machine learning models, including boosting algorithms. This allowed us to identify the most effective models for CKD detection.
4️⃣ Impressive Results 💯
Our hard work paid off! We achieved remarkable accuracy in our models:
6 models with a staggering 100% accuracy across all metrics
4 models with an impressive 98% accuracy
Our models outperformed the base paper's highest accuracy of 99.13%!
Additionally, our classification reports showed perfect scores in accuracy, recall, F1 score, and precision, highlighting the robustness of our approach.
5️⃣ Frontend & Backend Development 💻
To make this project user-friendly and accessible, we developed a sleek frontend using HTML, CSS, and JavaScript. On the backend, we used Flask to integrate everything, creating a seamless CKD detection system that’s both functional and easy to use.
🚀 Interested in Taking This Further?
If you're working on a similar project or want to explore this topic more deeply, we've got you covered! We're offering the full code, detailed documentation, and even mentorship to help you with any modifications you might need. Feel free to reach out to us at 808860568# or drop us a message on WhatsApp 📲. We're here to help you succeed!
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#MachineLearning #ChronicKidneyDisease #AI #CKDDetection #Python #Flask
