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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Heart Disease Risk Detection |
| Authors Name: | Akash Mandal , Madan Mohan Naidu , Arif Basha , Rachamalla Sriram |
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| Author Reg. ID: |
IJRTI_203028
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| Published Paper Id: | IJRTI2504277 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | Since heart disease is still one of the world’s top causes of death, effective and precise prediction models for early risk detection are required. Finding trends and risk factors linked to heart disease has shown encouraging outcomes when machine learning (ML) techniques are integrated into healthcare. In order to improve the accuracy of heart disease risk prediction models, this study investigates the use of Python programming, data preprocessing methods, and machine learning algorithms. The Cleveland Heart Disease dataset and other publicly accessible heart disease datasets served as the source of the dataset used in this investigation. Extensive data preprocessing is carried out, including feature selection, data balancing, normalization, and handling missing values, to guarantee optimal model performance. In order to convert unstructured data into useful features that raise prediction accuracy, feature engineering is essential. To improve the quality of the dataset, common methods including principal component analysis (PCA), min-max scaling, and onehot encoding are used. Individuals are categorized according to their risk of heart disease using a variety of machine learning methods. Examples of machine learning algorithms include Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Trees (DT), and Neural Networks. Standard performance criteria, like as accuracy, precision, recall, F1-score, and area under the curve (AUC-ROC), are used to evaluate these models once they have been thoroughly trained on preprocessed data. Hyperparameter tuning is done to improve model performance using approaches like Grid Search and Randomized Search Cross-Validation (CV). |
| Keywords: | : K-Nearest Neighbors (KNN), Random Forest (RF), Electrocardiogram (ECG), Principal Component Analysis (PCA),Accuracy, Precision, Recall, F1- score, Confusion Matrix, and Machine learning (ML) |
| Cite Article: | "Heart Disease Risk Detection", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.c456-c461, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504277.pdf |
| Downloads: | 000340 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504277
Registration ID:203028
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: c456-c461 Country: Hyderabad, Telangana, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504277 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504277 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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