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Detection of Cardiovascular Diseases Using Machine Learning Approach

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Advanced Computing (IACC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2054))

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Abstract

Various advanced computing techniques and capabilities have the deep impact in the field of medical sciences, especially in identifying human heart diseases. So that identifying heart related diseases accurately and in time may save the patients life’s and increases the chances of survival. However, manual approaches for identifying heart disease suffer from biases and variations between examiners. We can use various machine learning algorithms to overcome these issues in manual approaches These ML algorithms provide more accurate and efficient tools for identifying and analysing the patients with heart disease.

To explore the potential of machine learning algorithms, the recommended study employed various techniques to identify and predict human heart disease using a comprehensive heart disease dataset. Sensitivity, specificity, F-measure and accuracy in classification can be used to examine the performance. We have used eight machine learning classifiers such as Ada boost, Extreme Gradient Boosting including Decision Tree, Logistic Regression, Linear Discriminate Analysis, Random Forest, Naïve Bayes, Support Vector Machine. The results demonstrated notable improvements in the prediction classifiers’ accuracy. This underscores the efficiency of machine learning algorithms in finding and predicting human heart disease. This research achieved improved accuracy heart disease prediction using the machine learning technique. Multiple classifiers were employed to classify heart disease prediction, with SVM achieving an accuracy of 95.88%.

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Authors and Affiliations

  1. Symbiosis Institute of Technology, Affiliated to Symbiosis International University, Pune, India

    Amol Dhumane, Shwetambari Chiwhane & Pooja Bagane

  2. Pimpri Chinchwad College of Engineering, Pune, India

    Mubin Tamboli & Srinivas Ambala

  3. Vishwakarma Institute of Information Technology, Pune, India

    Vishal Meshram

Authors
  1. Amol Dhumane
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  2. Shwetambari Chiwhane
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  3. Mubin Tamboli
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  4. Srinivas Ambala
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  5. Pooja Bagane
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  6. Vishal Meshram
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Corresponding author

Correspondence to Amol Dhumane .

Editor information

Editors and Affiliations

  1. SR University, Warangal, India

    Deepak Garg

  2. COPELABS, Lusófona University, Lisbon, Portugal

    Joel J. P. C. Rodrigues

  3. Bennett University, Greater Noida, India

    Suneet Kumar Gupta

  4. Swansea University, Wales, UK

    Xiaochun Cheng

  5. Lovely Professional University, Phagwara, India

    Pushpender Sarao

  6. SITCOE Engineering College, Ichalkaranji, India

    Govind Singh Patel

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Dhumane, A., Chiwhane, S., Tamboli, M., Ambala, S., Bagane, P., Meshram, V. (2024). Detection of Cardiovascular Diseases Using Machine Learning Approach. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2054. Springer, Cham. https://doi.org/10.1007/978-3-031-56703-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-56703-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56702-5

  • Online ISBN: 978-3-031-56703-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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