Performance Evaluation Of SVM and ANN for Cancer Classification and Designing Algorithm for Arrhythmia Prediction in Cancer Patient

  • Authors

    • Chaithanya Lakshmi
    • Sachin Bojewar
    2018-06-21
    https://doi.org/10.14419/ijet.v7i3.3.14496
  • SVM, Neural Network, Classification, arrhythmia, Apache and SOFA score

  • Classifying the cancer based on the age and predicting the arrhythmia in cancer patient is necessary to determine the next steps in dealing with the patients. This prediction can be done by using multiple algorithms of machine learning such as SVM, Linear classifier, neural network. Machine learning, interpretability refers to understand the underlying behaviour of the prediction of a model in order to identify diagnosis criteria and/or new rules from its output. Interpretability contributes to increase the usability of the method. Also, it is relevant in decision support systems, such as in medical applications. Using multiple algorithm on big data set and predicting the arrhythmia cases from early age to old age.

    Apache (Acute Physiology, Age and Chronic Health Evaluation) and SOFA (Sequential Organ Failure Assessment) score are the important factor in critically ill patients. The number of ICU (intensive care unit) admission will be depending on these two scores. Analyzing Apache and SOFA scores will be helpful for intensivist.[4]

     

     

  • References

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    Lakshmi, C., & Bojewar, S. (2018). Performance Evaluation Of SVM and ANN for Cancer Classification and Designing Algorithm for Arrhythmia Prediction in Cancer Patient. International Journal of Engineering & Technology, 7(3.3), 99-101. https://doi.org/10.14419/ijet.v7i3.3.14496