A Deep Prediction of Chronic Kidney Disease by Employing Machine Learning Method
A Deep Prediction of Chronic Kidney Disease by Employing Machine Learning Method
IEEE BASE PAPER ABSTRACT:
In recent years, people worldwide have been suffering from various types of kidney diseases indescribably, among which chronic kidney disease (CKD) has exacerbated the situation. Early diagnosis of CKD is the only way to hinder the advancement of kidney disease in its initial stage. However, presently doctors can use machine learning classifier algorithms to identify the disease earlier than any other existing method. Here, this research work presents a method by using eight different machine learning (ML) algorithms that can promptly detect the infection of CKD considering the health condition dataset information of the patient. A dataset is used of nearly two months of that period delivered by the hospital to identify the plausibility of chronic kidney disease. This research study has used the Extra Tree Classifier (EXT), AdaBoost (ADB), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB) and Random Forest (RF) to obtain an optimum result of prediction. After preprocessing the data, this research work has applied the ML algorithms and compared their performances, and eventually, the precise outcome has been obtained. The performance is analyzed by using the F1-score, precision, accuracy, recall, and AUC score. According to the analysis results, K-Nearest Neighbors and Extra Tree Classifier have performed better than other algorithms for achieving an accuracy of 99% preceding the Gradient Boost, which stands at 98%.
OUR PROPOSED ABSTRACT:
Everyone is aware that the kidneys are a vital organ in the body, with primary functions like excretion and osmoregulation. Simply explained, the kidney and excretion system gather and eliminate all harmful and superfluous substances from the body. Chronic Kidney Disease is brought on by a kidney issue. Chronic kidney disease (CKD) is a non-communicable illness that affects 10-15% of the world’s population and has a considerable impact on morbidity, mortality, and hospital admission rates for patients worldwide.
To reduce the effects of the patient’s health difficulties, early and accurate detection of the phases of CKD is thought to be essential. A disorder known as chronic kidney disease (CKD) is defined by a long-term decline of kidney function. It depicts a medical condition that harms the kidneys and has an impact on a person’s overall health. End-stage renal disease and the patient’s eventual mortality can result from improper disease diagnosis and treatment. Many studies on the early identification of CKD have been conducted utilizing machine learning approaches.
This project aims to develop a system for predicting Chronic Kidney Disease (CKD) using machine learning method. Specifically, the proposed system employs an Artificial Neural Network (ANN) to predict CKD. The dataset used for training and testing the models is the Chronic Kidney Disease dataset from the UCI Machine Learning Repository.
The proposed system also built a web application using Flask framework where the users can enter the details and predict whether the CKD is there or not, which makes the system easier and accessible to every individual. The study contributes to the field of medical diagnosis and highlights the potential of using machine learning techniques for improving CKD prediction.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Artificial Neural Network (ANN).
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
SOFTWARE REQUIREMENTS:
- Operating System : Windows 10 / 11.
- Coding Language : Python 3.8.
- Web Framework : Flask.
- Frontend : HTML, CSS, JavaScript.
REFERENCE:
Deepanita Baidya, Umme Umaima, Md Nazrul Islam, F. M. Javed Mehedi Shamrat, Anik Pramanik, Md. Sadekur Rahman, “A Deep Prediction of Chronic Kidney Disease by Employing Machine Learning Method”, 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), IEEE Conference, 2022.