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Heart Disease Classification Using Machine Learning Models

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Informatics and Intelligent Applications (ICIIA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1547))

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Abstract

Heart Disease (HD) is a candidate for the utmost communal death-recording diseases in history and an early detection is a herculean task for countless physicians. This paper aims at developing a precise and efficient machine learning (ML) classification model for HD. The HD dataset was subjected to seven different machine learning models, including k-Nearest Neighbour (k-NN), eXtreme Gradient Boosting (XGBoost), Extra Trees (ET), Decision Tree (DT), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), and Random Forest (RF). Recall, precision, F1-Score, accuracy, ROC, and RPC were all used to evaluate the proposed models. The results obtained based on the aforementioned metrics in comparison to other models indicate that ET performed better. ET achieved 87% accuracy, precision (0.88), RPC (0.86), Recall (0.87), ROC (0.94), and F1-score (0.935) respectively. The outcomes indicates that ML models can classify HD patients effectively.

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Notes

  1. 1.

    https://www.kaggle.com/johnsmith88/heart-disease-dataset.

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Authors and Affiliations

  1. Department of Mathematical Science, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

    Sakinat Oluwabukonla Folorunso

  2. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde

  3. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Emmanuel Abidemi Adeniyi & Kazeem Moses Abiodun

  4. Department of Computer Science, McPherson University, Seriki-Sotayo, Abeokuta, Nigeria

    Femi Emmanuel Ayo

  5. Life on Land Research Group, Landmark University SDG 15, Omu-Aran, Nigeria

    Kazeem Moses Abiodun

  6. Quality Education Group, Landmark University SDG 4, Omu-Aran, Nigeria

    Emmanuel Abidemi Adeniyi & Kazeem Moses Abiodun

  7. Industry, Innovation and Infrastructure Research Group, Landmark University SDG 9, Omu-Aran, Nigeria

    Emmanuel Abidemi Adeniyi

Authors
  1. Sakinat Oluwabukonla Folorunso
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  2. Joseph Bamidele Awotunde
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  3. Emmanuel Abidemi Adeniyi
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  4. Kazeem Moses Abiodun
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  5. Femi Emmanuel Ayo
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Corresponding author

Correspondence to Joseph Bamidele Awotunde .

Editor information

Editors and Affiliations

  1. Østfold University College, Halden, Norway

    Sanjay Misra

  2. Covenant University, Ota, Nigeria

    Jonathan Oluranti

  3. Silesian University of Technology, Gliwice, Poland

    Robertas Damaševičius

  4. Kaunas University of Technology, Kaunas, Lithuania

    Rytis Maskeliunas

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Folorunso, S.O., Awotunde, J.B., Adeniyi, E.A., Abiodun, K.M., Ayo, F.E. (2022). Heart Disease Classification Using Machine Learning Models. In: Misra, S., Oluranti, J., Damaševičius, R., Maskeliunas, R. (eds) Informatics and Intelligent Applications. ICIIA 2021. Communications in Computer and Information Science, vol 1547. Springer, Cham. https://doi.org/10.1007/978-3-030-95630-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-95630-1_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-95630-1

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