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Machine Learning-Based Early Diabetes Prediction

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Intelligent Sustainable Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 213))

Abstract

There are several diseases that the world faces presently and a critical one is Diabetes mellitus. The current diagnostic practice involves various tests at a lab or a hospital and a treatment based on the outcome of the diagnosis. This study proposes a machine learning model to classify a patient as diabetic or not, utilizing the popular PIMA Indian Dataset. The dataset contains features like Pregnancy, Blood Pressure, Skin Thickness, Age and Diabetes Pedigree Function along with regular factors like Glucose, BMI and Insulin. The objective of this study is to make use of several pre-processing techniques resulting in improved accuracy over simple models. The study compares different classification models namely GaussianNB, Logistic Regression, KNN, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier in several ways. Initially, missing values in the significant features are replaced by computing median of the input variables based on the outcome of whether the patient is diabetic or not. After this, feature engineering is performed by adding new features which are obtained by categorizing the existing features based on its range. Finally, Hyperparameter tuning is carried out to optimize the model. Performance metrics such as Accuracy and area under the ROC Curve (AUC) is used to validate the effectiveness of the proposed framework. Results indicate that XGBoosting Classifier is concluded as the optimum model with 88% accuracy and AUC value of 0.948. The performance of the model is evaluated using Confusion Matrix and ROC Curve.

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Author information

Authors and Affiliations

  1. Amrita School of Arts and Science, Amrita Vishwa Vidyapeetham, Kochi Campus, Kochi, India

    Deepa Elizabeth James & E. R. Vimina

Authors
  1. Deepa Elizabeth James
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  2. E. R. Vimina
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Corresponding author

Correspondence to Deepa Elizabeth James .

Editor information

Editors and Affiliations

  1. Gnanmani College of Engineering and Technology, Namakkal, Tamil Nadu, India

    Jennifer S. Raj

  2. Department of Business Administration, The Gerald Schwartz, School of Business, St. Francis Xavier University, Nova Scotia, NS, Canada

    Ram Palanisamy

  3. Department of Computer Engineering and Informatics, University of Patras, Patra, Greece

    Isidoros Perikos

  4. Department of Computer Science, Kennesaw State University, Kennesaw, GA, USA

    Yong Shi

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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James, D.E., Vimina, E.R. (2022). Machine Learning-Based Early Diabetes Prediction. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_52

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