Title:On the Improvements of the Performance of Bayes-Net Classification for
Diagnosis of Diabetes: A Cross-validation Approach
Volume: 18
Issue: 2
Author(s): Sarvachan Verma*, Anupama Sharma, Sachin Jain and Anuj Kumar
Affiliation:
- Information Technology, Ajay Kumar Garg Engineering College, Ghaziabad, India
Keywords:
Accuracy, classification, diabetes, machine learning, autoimmune condition, logistic regression.
Abstract:
Introduction: This study intends to support medical professionals in the early and precise
detection and diagnosis of type1 and type2 Diabetes. Many complexities occur if Diabetes is
unidentified and remains untreated. 9.3% of individuals worldwide have Diabetes as of 2019, according
to data from the World Health Organization. The anticipated increase would be close to
11% of the global population by 2045.
Diabetes mellitus is a chronic, one of the deadliest diseases that causes Body blood glucose levels
to be above average level knowing a blood sugar. Diabetes can be type1 (T1D) and type2(T2D).
An autoimmune condition called Type 1 diabetes is frequently found in young children. Adults
are frequently diagnosed with type 2 diabetes, which is not autoimmune. A branch of artificial
intelligence called machine learning has the potential to be successful in the field of predictions.
Method: This research aims to design a machine learning model to predict Diabetes in a patient.
The collective performance of the four algorithms is evaluated on various measures like accuracy,
precision, sensitivity, and specificity. Therefore, four machine learning classification algorithms,
namely Bayes-Net, Naïve-Bayes, J48, and Random Forest, were used to predict Diabetes
in the early stage using 10–fold cross validation.
Result: FFor experiments, a standard PIMA Indian diabetes dataset is used. According to the results,
Bayes-Net performs better than other algorithms, with an accuracy rate of 87.72%.
Conclusion: Diabetes Mellitus may harm kidneys, nerves, eyes, and other organs if it is not diagnosed
in time. Therefore, early diagnosis and treatment could save countless lives. The process
is tedious because patients visit a diagnostic facility and see a doctor, but various machine learning
and deep learning approaches solve this clinical problem earlier.
