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Predicting the Onset of Diabetes Using Multimodal Data and a Novel Machine Learning Method

EasyChair Preprint no. 10149

15 pagesDate: May 13, 2023


Diabetes is a chronic or Deep-seated, Diabetes is a disease that occur when blood glucose is too high. Blood glucose is the source of Energy and it comes from food. As per world health organization, in 2019 Diabetes caused 1.5 million deaths.Most of these deaths occurred in low- and middle-income countries. As per MedicalNewsToday report more than 37 million Trusted Source adults are living with diabetes in the United States andthat has more than doubled in the last two decades.To reduce large Scale of death rate from diabetes a quick and efficient technique is to be deserved. Machine learning has a very crucial role in healthcare industry for prediction and analyse made by using machine learning requires different medical datasets.

         The PIMA Indians diabetes dataset is used in this paper, which contain the information of patients affected and non-affected with diabetes.Our approach involves feature selection and data pre-processing, followed by the training of various ML models, including Random Forest(Rf), Support Vector Machine(SVM),  Logistic Regression(LR), K-nearest neighbour, Decision Tree.

         The main objective of this research project is to predict the diabetes of a patient using machine learning algorithm. The early detection and prevention of diabetes complications are critical for improving health outcomes and reducing healthcare costs. Machine learning algorithms have shown promising results in predicting the risk of complications in patients with diabetes.

Keyphrases: KNN, LR, ML, RF, SVM

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Manjushree Nayak and Jagannath Tiyadi},
  title = {Predicting the Onset of Diabetes Using Multimodal Data and a Novel Machine Learning Method},
  howpublished = {EasyChair Preprint no. 10149},

  year = {EasyChair, 2023}}
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