Chronic Kidney Disease (CKD) is a worldwide
concern that influences roughly 10% of the grown-up population
on the world. For most of the people the early diagnosis of CKD is
often not possible. Therefore, the utilization of present-day
Computer aided supported strategies is important to help the
conventional CKD finding framework to be progressively effective
and precise. In this project, six modern machine learning
techniques namely Multilayer Perceptron Neural Network,
Support Vector Machine, Naïve Bayes, K-Nearest Neighbor,
Decision Tree, Logistic regression were used and then to enhance
the performance of the model Ensemble Algorithms such as
ADABoost, Gradient Boosting, Random Forest, Majority Voting,
Bagging and Weighted Average were used on the Chronic Kidney
Disease dataset from the UCI Repository. The model was tuned
finely to get the best hyper parameters to train the model. The
performance metrics used to evaluate the model was measured
using Accuracy, Precision, Recall, F1-score, Mathew`s
Correlation Coefficient and ROC-AUC curve. The experiment
was first performed on the individual classifiers and then on the
Ensemble classifiers. The ensemble classifier like Random Forest
and ADABoost performed better with 100% Accuracy, Precision
and Recall when compared to the individual classifiers with
99.16% accuracy, 98.8% Precision and 100% Recall obtained
from Decision Tree Algorithm
Development of a Safety Management System Tracking the Weight of Heavy Objects Carried by Construction Workers Using FSR Sensors
