Cite this DOI

Reddy Gurram, Y., & Lakshmi, S. (2021). Prediction of Heart Disease Using Machine Learning Algorithms. *Journal of Science & Technology*, *06*(05), 12–15. https://doi.org/10.46243/jst.2021.v6.i05.pp12-15

@article{reddygurram2021prediction,
  author    = {Reddy Gurram, Yeshwanth and Lakshmi, S.Vijaya},
  title     = {{Prediction of Heart Disease Using Machine Learning Algorithms}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {sep},
  volume    = {06},
  number    = {05},
  pages     = {12--15},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i05.pp12-15},
  url       = {https://doi.org/10.46243/jst.2021.v6.i05.pp12-15},
  language  = {en},
  abstract  = {Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.}
}

TY  - JOUR
TI  - Prediction of Heart Disease Using Machine Learning Algorithms
AU  - Reddy Gurram, Yeshwanth
AU  - Lakshmi, S.Vijaya
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/09/20/
VL  - 06
IS  - 05
SP  - 12
EP  - 15
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.
DO  - 10.46243/jst.2021.v6.i05.pp12-15
UR  - https://doi.org/10.46243/jst.2021.v6.i05.pp12-15
ER  -

{
    "type": "article-journal",
    "id": "10.46243/jst.2021.v6.i05.pp12-15",
    "DOI": "10.46243/jst.2021.v6.i05.pp12-15",
    "URL": "https://doi.org/10.46243/jst.2021.v6.i05.pp12-15",
    "title": "Prediction of Heart Disease Using Machine Learning Algorithms",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Reddy Gurram",
            "given": "Yeshwanth"
        },
        {
            "family": "Lakshmi",
            "given": "S.Vijaya"
        }
    ],
    "issued": {
        "date-parts": [
            [
                2021,
                9,
                20
            ]
        ]
    },
    "volume": "06",
    "issue": "05",
    "page": "12-15",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "Heart disease is the one of the most common disease. This disease is quite common now a days we used different attributes which can relate to this heart diseases well to find the better method to predict and we also used algorithms for prediction. Naive Bayes, algorithm is analyzed on dataset based on risk factors. We also used decision trees and combination of algorithms for the prediction of heart disease based on the above attributes. The results shown that when the dataset is small naive Bayes algorithm gives the accurate results and when the dataset is large decision trees gives the accurate results.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i05.pp12-15 gives all four in one JSON answer.