Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm

2020 ◽  
Author(s):  
Pronab Ghosh ◽  
F. M. Javed Mehedi Shamrat ◽  
Shahana Shultana ◽  
Saima Afrin ◽  
Atqiya Abida Anjum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Machine Learning Algorithm

scholarly journals Prediction of Chronic Kidney Disease Using Machine Learning Algorithm

IJARCCE ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 92-96 ◽  
Author(s):  
Siddheshwar Tekale ◽  
Pranjal Shingavi ◽  
Sukanya Wandhekar
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

Predicting Chronic Kidney Disease Using KNN Algorithm

2021 ◽  
Vol 1 (2) ◽  
pp. 16-24
Author(s):  
V Mareeswari ◽  
Sunita Chalageri ◽  
Kavita K Patil
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Learning Algorithm ◽  
University Of California ◽  
Learning Models ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
The University ◽  
Machine Learning Models

Chronic kidney disease (CKD) is a world heath issues, and that also includes damages and can’t filter blood the way it should be. since we cannot predict the early stages of CKD, patience will fail to recognise the disease. Pre detection of CKD will allow patience to get timely facility to ameliorate the progress of the disease. Machine learning models will effectively aid clinician’s progress this goal because of the early and accurate recognition performances. The CKD data set is collected from the University of California Irvine (UCI) Machine Learning Recognition. Multiple Machine and deep learning algorithm used to predict the chronic kidney disease.

Diagnosis Rule Extraction from Patient Data for Chronic Kidney Disease Using Machine Learning

2016 ◽  
Vol 5 (2) ◽  
pp. 64-72 ◽  
Author(s):  
Alexander Arman Serpen
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Learning Algorithm ◽  
Primary Care Providers ◽  
Patient Data ◽  
Care Providers ◽  
Data Set ◽  
Actual Patient

This research study employed a machine learning algorithm on actual patient data to extract decision making rules that can be used to diagnose chronic kidney disease. The patient data set entails a number of health-related attributes or indicators and contains 250 patients positive for chronic kidney disease. The C4.5 decision tree algorithm was applied to the patient data to formulate a set of diagnosis rules for chronic kidney disease. The C4.5 algorithm utilizing 3-fold cross validation achieved 98.25% prediction accuracy and thus correctly classified 393 instances and incorrectly classified 7 instances for a total patient count of 400. The extracted rule set highlighted the need to monitor serum creatinine levels in patients as the primary indicator for the presence of disease. Secondary indicators were pedal edema, hemoglobin, diabetes mellitus and specific gravity. The set of rules provides a preliminary screening tool towards conclusive diagnosis of the chronic kidney disease by nephrologists following timely referral by the primary care providers or decision-making algorithms.

Diagnosis Rule Extraction from Patient Data for Chronic Kidney Disease Using Machine Learning

2020 ◽  
pp. 1165-1174
Author(s):  
Alexander Arman Serpen
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Decision Making ◽  
Kidney Disease ◽  
Learning Algorithm ◽  
Patient Data ◽  
Actual Patient ◽  
C4.5 Decision Tree ◽  
Health Related ◽  
Pedal Edema

This research study employed a machine learning algorithm on actual patient data to extract decision making rules that can be used to diagnose chronic kidney disease. The patient data set entails a number of health-related attributes or indicators and contains 250 patients positive for chronic kidney disease. The C4.5 decision tree algorithm was applied to the patient data to formulate a set of diagnosis rules for chronic kidney disease. The C4.5 algorithm utilizing 3-fold cross validation achieved 98.25% prediction accuracy and thus correctly classified 393 instances and incorrectly classified 7 instances for a total patient count of 400. The extracted rule set highlighted the need to monitor serum creatinine levels in patients as the primary indicator for the presence of disease. Secondary indicators were pedal edema, hemoglobin, diabetes mellitus and specific gravity. The set of rules provides a preliminary screening tool towards conclusive diagnosis of the chronic kidney disease by nephrologists following timely referral by the primary care providers or decision-making algorithms.

scholarly journals Machine learning approach using hierarchical decision- tree projection algorithm to predict the presence of chronic kidney disease

2021 ◽  
Vol 8 (2) ◽  
pp. 088-095
Author(s):  
J Sarada ◽  
NV Muthu Lakshmi
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Human Body ◽  
Learning Algorithm ◽  
Human Study ◽  
Projection Algorithm ◽  
Study Results ◽  
Hierarchical Decision

Chronic Kidney disease is one of the eminent diseases which is commonly seen the patients which the various ailments which results in the step by step failure of kidneys which may result in the fatality of the human. The Chronic Kidney disease which is precisely called as CKD in medical terms is predicted with various symptoms that evolves in the human body. Strategically predicting the CKD using the machine learning algorithm is the challenging proportion. This paper solves the issues of predicting the CKD using the Hierarchical Decision-Tree Projection Algorithm which takes the various clinical study results of the human body into the dataset format and is algorithmically evaluated. Through this method the whole study in completely evaluated with the various parameters which is obtained from the human study. The variations in the whole study with respect to the parametrical correlation are taken into consideration. The results are obtained from each parametrical evaluation and with the results the prediction and presence of the Chronic Kidney disease is evaluated. The Experimental results show the algorithmic evaluations are showing the comparatively high accuracy and performance.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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