Predicting Diabetes Mellitus With Machine Learning Techniques

By Quan Zou, Kaiyang Qu, Ya-ling Luo, Dehui Yin, Ying Ju, Hua Tang. Frontiers in Genetics, 2018

767 citations recorded by the source corpus.

Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients’ data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used.

Subfields: Artificial Intelligence, Health Information Management, Information Systems

View Paper: https://www.semanticscholar.org/paper/5327bb691a1c63791a06de2d3f0478e47785add5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Adnan Mohsin Abdulazeez
2. Bahzad Charbuty
3. D. M. Abdullah

In Health Information Management

1. Adnan Mohsin Abdulazeez
2. Bahzad Charbuty
3. D. M. Abdullah

In Information Systems

1. Bahzad Charbuty
2. Adnan Mohsin Abdulazeez

Citations in the corpus

• Classification Based on Decision Tree Algorithm for Machine Learning (2021, methodology): “[64] Utilized a decision tree (j48), Random Forest (RF), and neural network algorithms for diabetes mellitus prediction.”
• Classification and prediction of diabetes disease using machine learning paradigm (2020, background): “Zou et al. [47] studied on the diagnosis of diabetes dataset.”