Cite this DOI

Bobburi Naga Vardhana, Basati Rohitha, Gunti Anusha, Bulla Sneha Latha, Chilaka Aiswarya, & Mr. R. Sudha Kishore (2024). HARNESSING DEEP NEURAL NETWORKS FOR HEART DISEASE PREDICTION. *Journal of Science & Technology*, *09*(04), 01. https://doi.org/10.46243/jst.2024.v9.i4.pp1-9

@article{bobburinagavardhana2024harnessing,
  author    = {Bobburi Naga Vardhana and Basati Rohitha and Gunti Anusha and Bulla Sneha Latha and Chilaka Aiswarya and Mr. R. Sudha Kishore},
  title     = {{HARNESSING DEEP NEURAL NETWORKS FOR HEART DISEASE PREDICTION}},
  journal   = {Journal of Science \& Technology},
  year      = {2024},
  month     = {apr},
  volume    = {09},
  number    = {04},
  pages     = {01},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2024.v9.i4.pp1-9},
  url       = {https://doi.org/10.46243/jst.2024.v9.i4.pp1-9},
  language  = {en},
  abstract  = {Making forecasts and diagnosing ailments has never been simple for medical professionals when it comes to heart conditions. Cardiovascular disease medical professionals have always found it difficult to predict and diagnose. As a result, being able to people all around the world can take the necessary actions to treat cardiac disease before it becomes severe if it is discovered in its early stages. The main causes of heart disease, a severe problem in recent years, are drinking alcohol, smoking cigarettes, and not exercising. A significant amount of data generated over time by the health care sector has allowed machine learning to offer efficient results in decision-making and prediction. Healthcare is basic to human well-being, and the industry collects an expansive sum of psychiatric information. Machine learning models are being utilized to move forward the precision of heart illness forecast. These models permit people to be dependably classified as sound or unfortunate. Our think about presented a comprehensive system that gets it the standards included in anticipating patients' chance profiles utilizing clinical information parameters. The proposed appearutilizes a Significant Neural Orchestrate to effectively address issues of underfitting and overfitting. This illustrate outflanks on both test and planning data. The model's effectiveness was encouraging inspected utilizing both Profound Neural Arrange (DNN) and Manufactured Neural Arrange (ANN) approaches, coming about in exact expectations of the nearness or nonappearance of heart illness.}
}

TY  - JOUR
TI  - HARNESSING DEEP NEURAL NETWORKS FOR HEART DISEASE PREDICTION
AU  - Bobburi Naga Vardhana
AU  - Basati Rohitha
AU  - Gunti Anusha
AU  - Bulla Sneha Latha
AU  - Chilaka Aiswarya
AU  - Mr. R. Sudha Kishore
JO  - Journal of Science & Technology
PY  - 2024
DA  - 2024/04/05/
VL  - 09
IS  - 04
SP  - 01
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Making forecasts and diagnosing ailments has never been simple for medical professionals when it comes to heart conditions. Cardiovascular disease medical professionals have always found it difficult to predict and diagnose. As a result, being able to people all around the world can take the necessary actions to treat cardiac disease before it becomes severe if it is discovered in its early stages. The main causes of heart disease, a severe problem in recent years, are drinking alcohol, smoking cigarettes, and not exercising. A significant amount of data generated over time by the health care sector has allowed machine learning to offer efficient results in decision-making and prediction. Healthcare is basic to human well-being, and the industry collects an expansive sum of psychiatric information. Machine learning models are being utilized to move forward the precision of heart illness forecast. These models permit people to be dependably classified as sound or unfortunate. Our think about presented a comprehensive system that gets it the standards included in anticipating patients' chance profiles utilizing clinical information parameters. The proposed appearutilizes a Significant Neural Orchestrate to effectively address issues of underfitting and overfitting. This illustrate outflanks on both test and planning data. The model's effectiveness was encouraging inspected utilizing both Profound Neural Arrange (DNN) and Manufactured Neural Arrange (ANN) approaches, coming about in exact expectations of the nearness or nonappearance of heart illness.
DO  - 10.46243/jst.2024.v9.i4.pp1-9
UR  - https://doi.org/10.46243/jst.2024.v9.i4.pp1-9
ER  -

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    "DOI": "10.46243/jst.2024.v9.i4.pp1-9",
    "URL": "https://doi.org/10.46243/jst.2024.v9.i4.pp1-9",
    "title": "HARNESSING DEEP NEURAL NETWORKS FOR HEART DISEASE PREDICTION",
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            "family": "Bobburi Naga Vardhana"
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        {
            "family": "Basati Rohitha"
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        {
            "family": "Gunti Anusha"
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            "family": "Bulla Sneha Latha"
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            "family": "Chilaka Aiswarya"
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    "language": "en",
    "abstract": "Making forecasts and diagnosing ailments has never been simple for medical professionals when it comes to heart conditions. Cardiovascular disease medical professionals have always found it difficult to predict and diagnose. As a result, being able to people all around the world can take the necessary actions to treat cardiac disease before it becomes severe if it is discovered in its early stages. The main causes of heart disease, a severe problem in recent years, are drinking alcohol, smoking cigarettes, and not exercising. A significant amount of data generated over time by the health care sector has allowed machine learning to offer efficient results in decision-making and prediction. Healthcare is basic to human well-being, and the industry collects an expansive sum of psychiatric information. Machine learning models are being utilized to move forward the precision of heart illness forecast. These models permit people to be dependably classified as sound or unfortunate. Our think about presented a comprehensive system that gets it the standards included in anticipating patients' chance profiles utilizing clinical information parameters. The proposed appearutilizes a Significant Neural Orchestrate to effectively address issues of underfitting and overfitting. This illustrate outflanks on both test and planning data. The model's effectiveness was encouraging inspected utilizing both Profound Neural Arrange (DNN) and Manufactured Neural Arrange (ANN) approaches, coming about in exact expectations of the nearness or nonappearance of heart illness.",
    "ISSN": "2456-5660"
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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.2024.v9.i4.pp1-9 gives all four in one JSON answer.