ELECTRONIC HEALTH RECORD STAGE IDENTIFICATION USING PRINCIPAL COMPONENT ANALYSIS



Authors

  • 1Karumanchi Hari Babu 2Dr. P Harini 3N Lakshmi Narayana

DOI:

https://doi.org/10.15282/jmes.17.1.2023.10.0759


Keywords:

Chronic Kidney Disease, Decision Tree, Machine Learning, Random Forest, Support Vactor s, chronic kidney infection; machine learning, and CNN, support vector machine.


Abstract

The field of biosciences has advanced to larger extent and has generated large amounts of information from Electronic Health Records. This has given rise to the acute need of knowledge generation from this enormous amount of data. Convolutional Neural Networks (CNN) and Principal Component Analysis (PCA) to predict the likelihood of CKD in patients and classify their stage of disease. This paper discusses the categorization of CKD using AI representation. To forecast the condition of chronic kidney disease, a large number of researches have been done using machine learning and deep learning techniques. The early identification of chronic kidney disease in HIV infected patients seems to be more fruitful as they will get appropriate treatment at early stage. With the availability of pathology data, the use of machine-learning techniques in healthcare for classification and prediction of disease has become more common. This paper presents the classification of CKD using machine learning models. In grouping CKD patients with HIV the DNN model outperforms with the vast majority of precision. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The system was evaluated on a dataset of patients with varying stages of CKD and achieved high accuracy and stage classification performance, demonstrating its potential as an early detection and treatment aid for CKD. This paper uses data preprocessing, data transformation and various classifiers to predict CKD and also proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD.



Published

2024-06-30

How to Cite