Predictive Modeling of Chronic Kidney Disease Using Extra Tree Classifier: A Comparative Analysis with Traditional Methods
Clicks: 1
ID: 313085
2024
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #586 of 705 articles by views in Journal of Computing & Biomedical Informatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
With a high prevalence of morbidity and death, chronic renal illness is a major global health concern. Conventional diagnostic methods frequently miss the disease until it has grown to an advanced stage, despite the fact that prompt diagnosis and treatment can greatly improve patient outcomes. This work suggests a unique method for utilizing machine learning (ML) algorithms to identify kidney sickness, which might offer a solution to this urgent healthcare problem. One of the many industries where machine learning—a subset of artificial intelligence—has demonstrated great potential is healthcare. Due of its capacity to make predictions and Take note of the data, it is a useful tool for predicting illnesses. This study uses a variety of clinical indicators along with machine learning methods to predict when chronic kidney disease (CKD) will manifest. The proposed model uses a dataset comprising numerous patient records with various attributes. These attributes are used as input features for the machine learning algorithm. The target variable is the presence or absence of chronic kidney disease. A number of machine learning algorithms are used, and their performances are contrasted, including KNN, Chronic Kidney Disease, Machine Learning, Gradient Boosting Classifier, Ada Boost Classifier, Random Forest Classifier, XgBoost, Cat Boost, and Extra Trees Classifier. To assess each algorithm's predictive accuracy, sensitivity, specificity, and other performance metrics, a subset of the dataset is used for training, and afterwards the algorithm is tested using untested data. The findings show that machine learning algorithms, some of which are more accurate than others, can predict chronic kidney disease. According to these results, machine learning may prove to be a useful tool in the early diagnosis of chronic kidney disease, allowing for prompt intervention and maybe leading to better patient outcomes.
| Reference Key |
imported_1777058921_69ebc4694f719
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Umair Abid |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2024 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.