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Comparative Analysis of Permanent Neonatal Diabetes Mellitus Prediction using various Machine Learning Techniques

by Madhura Devendra Ranade
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 23
Year of Publication: 2024
Authors: Madhura Devendra Ranade
10.5120/ijca2024923682

Madhura Devendra Ranade . Comparative Analysis of Permanent Neonatal Diabetes Mellitus Prediction using various Machine Learning Techniques. International Journal of Computer Applications. 186, 23 ( May 2024), 50-53. DOI=10.5120/ijca2024923682

@article{ 10.5120/ijca2024923682,
author = { Madhura Devendra Ranade },
title = { Comparative Analysis of Permanent Neonatal Diabetes Mellitus Prediction using various Machine Learning Techniques },
journal = { International Journal of Computer Applications },
issue_date = { May 2024 },
volume = { 186 },
number = { 23 },
month = { May },
year = { 2024 },
issn = { 0975-8887 },
pages = { 50-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number23/comparative-analysis-of-permanent-neonatal-diabetes-mellitus-prediction-using-various-machine-learning-techniques/ },
doi = { 10.5120/ijca2024923682 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-05-31T22:32:03.075476+05:30
%A Madhura Devendra Ranade
%T Comparative Analysis of Permanent Neonatal Diabetes Mellitus Prediction using various Machine Learning Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 23
%P 50-53
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper aims at analyzing the machine learning techniques for prediction of Permanent Neonatal Diabetes Mellitus (PNDM). PNDM is a serious condition responsible for neonatal mortality. This can be caused due to insufficient insulin levels. The newborn baby in its month after birth is termed as “neonate”. One of the Sustainable development goals provided by world health organization is to reduce neonatal mortality. Machine learning approach for disease prediction is a noninvasive method of dealing with the available data and its interpretation. The main objective of the paper is to assess the best suitable machine learning method for the prediction of PNDM. The dataset used in the analysis consists of six input features as age, information related to genetics, HbA1c level, medical attributes, laboratory details and maternal history. The output feature or target variable in this dataset is the presence or absence of PNDM. [1]. The dataset is divided in training and testing modules in the ratio of 8:2. The dataset is validated and tested using various machine learning techniques such as Decision Tree classifier, Support Vector Machines, Logistic Regression, Naïve Bayes and Ensemble classifier. It was observed that, tree classifier was the best suitable choice for prediction of PNDM as it provided 99% accuracy and lowest training and validation cost as compared to other methods.

References
  1. https://www.kaggle.com/datasets/slmsshk/pndm-prediction-dataset
  2. Barbarini DS, Haslinger V, Schmidt K, Patch AM, Muller G, Simma B. Neonatal diabetes mellitus due to pancreas agenesis: a new case report and review of the literature. Pediatr Diabetes. 2009 Nov;10(7):487-91. doi: 10.1111/j.1399-5448.2009.00523.x. Epub 2009 Jun 3.
  3. Tasin I, Nabil TU, Islam S, Khan R. Diabetes prediction using machine learning and explainable AI techniques. Healthc Technol Lett. 2022 Dec 14;10(1-2):1-10. doi: 10.1049/htl2.12039. PMID: 37077883; PMCID: PMC10107388..
  4. Zhou, H., Myrzashova, R. & Zheng, R. Diabetes prediction model based on an enhanced deep neural network. J Wireless Com Network 2020, 148 (2020). https://doi.org/10.1186/s13638-020-01765-7.Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
Index Terms

Computer Science
Information Sciences
Machine Learning Disease prediction

Keywords

Permanent Neonatal Diabetes Mellitus (PNDM) machine learning neonatal healthcare decision tree classifier SVM classifier.