Performance Analysis of Classification Algorithms on Birth Dataset

Abstract

Generating intuitions from data using data mining and machine learning algorithms to predict outcomes is useful area of computing. The application area of data mining techniques and machine learning is wide ranging including industries, healthcare, organizations, academics etc. A continuous improvement is witnessed due to an ongoing research, as seen particularly in healthcare. Several researchers have applied machine learning to develop decision support systems, perform analysis of dominant clinical factors, extraction of useful information from hideous patterns in historical data, making predictions and disease classification. Successful researches created opportunities for physicians to take appropriate decision at right time. In current study, we intend to utilize the learning capability of machine learning methods towards the classification of birth data using bagging and boosting classification algorithms. It is obvious that differences in living styles, medical assistances, religious implications and the region you live in collectively affect the residents of that society. This motive has encouraged the researchers to conduct studies at regional levels to comprehensively explore the associated medical factors that contribute towards complications among women during pregnancy. The current study is a comprehensive comparison of bagging and boosting classification algorithms performed on birth data collected from the government hospitals of city Muzaffarabad, Kashmir. The experimental tasks are carried out using caret package in R which is considered an inclusive framework for building machine learning models. Accuracy based results with different evaluation measures are presented. Bagging functions including Adabag and BagFda performed marginally better in terms of accuracy, precision and recall. Improvements are observed in comparison to previous study performed on same dataset.

CovMedCare: Confluence of Internet of Things, Blockchain and Machine Learning for Remote Monitoring System of Pandemic Patients

“…WBAN is crucial in keeping track of the patient's vitals for severity prediction risk [3,4]. Various authors have proposed many disease prediction algorithms using several machine learning algorithms [5][6][7]. These classi cation models, such as Support Vector Machine (SVM), Linear Regression (LR), Decision Tree (DT), Gaussian Naive Bayes (GN), and Random Forest (RF), were applied to several healthcare datasets for disease prediction [8][9][10].…”

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

“…Many researchers have developed various disease prediction models using different machine learning classification algorithms [15] , [16] , [17] . Several classification models including linear regression (LR), Support Vector Clustering (SVC), Decision Tree (DT), Random Forest (RF), and Gaussian Naive Bayes (GN) were analyzed and tested using various healthcare datasets for disease prediction [18] , [19] , [20] .…”

K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad, Kashmir

“…The mode of the delivery (Caesarean or normal) is the outcome variable of the data. This variable makes the data suitable to conduct classification studies as well [41], [42]. Few attributes from the data set used in experiments are provided in table 1.…”