Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance
Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-07-01
|
Series: | Technologies |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7080/9/3/52 |
id |
doaj-56781786dd5e4c53aa34aded6b16b12e |
---|---|
record_format |
Article |
spelling |
doaj-56781786dd5e4c53aa34aded6b16b12e2021-09-26T01:32:21ZengMDPI AGTechnologies2227-70802021-07-019525210.3390/technologies9030052Effect of Data Scaling Methods on Machine Learning Algorithms and Model PerformanceMd Manjurul Ahsan0M. A. Parvez Mahmud1Pritom Kumar Saha2Kishor Datta Gupta3Zahed Siddique4School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USASchool of Engineering, Deakin University, Waurn Ponds, VIC 3216, AustraliaMewbourne College of Earth and Energy, University of Oklahoma, Norman, OK 73019, USADepartment of Computer Science, University of Memphis, Memphis, TN 38111, USASchool of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USAHeart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.https://www.mdpi.com/2227-7080/9/3/52heart diseasemachine learning algorithmdata scalingpredictionautomated model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Md Manjurul Ahsan M. A. Parvez Mahmud Pritom Kumar Saha Kishor Datta Gupta Zahed Siddique |
spellingShingle |
Md Manjurul Ahsan M. A. Parvez Mahmud Pritom Kumar Saha Kishor Datta Gupta Zahed Siddique Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance Technologies heart disease machine learning algorithm data scaling prediction automated model |
author_facet |
Md Manjurul Ahsan M. A. Parvez Mahmud Pritom Kumar Saha Kishor Datta Gupta Zahed Siddique |
author_sort |
Md Manjurul Ahsan |
title |
Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance |
title_short |
Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance |
title_full |
Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance |
title_fullStr |
Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance |
title_full_unstemmed |
Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance |
title_sort |
effect of data scaling methods on machine learning algorithms and model performance |
publisher |
MDPI AG |
series |
Technologies |
issn |
2227-7080 |
publishDate |
2021-07-01 |
description |
Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method. |
topic |
heart disease machine learning algorithm data scaling prediction automated model |
url |
https://www.mdpi.com/2227-7080/9/3/52 |
work_keys_str_mv |
AT mdmanjurulahsan effectofdatascalingmethodsonmachinelearningalgorithmsandmodelperformance AT maparvezmahmud effectofdatascalingmethodsonmachinelearningalgorithmsandmodelperformance AT pritomkumarsaha effectofdatascalingmethodsonmachinelearningalgorithmsandmodelperformance AT kishordattagupta effectofdatascalingmethodsonmachinelearningalgorithmsandmodelperformance AT zahedsiddique effectofdatascalingmethodsonmachinelearningalgorithmsandmodelperformance |
_version_ |
1716868767817924608 |