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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Volume 9 | Issue 5
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| Paper Title: | Mutiple Disease Detection using Machine Learning |
| Authors Name: | Sai Vamsi Chandhavari , Rama Mohan Reddy G , Pavan Kumar K , Naga Yaswanth Reddy D |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_219094
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| Published Paper Id: | IJNRD2404720 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Effective detection of illnesses is an influential unmet need absent from a global scale. A rise of early diagnosis tools and a successful therapy options has been seriously compromised by the complexity of the different conditions mechanisms and underlying symptoms that impacted the patient population. A subfield of artificial intelligence called machine learning (ML) helps patients, doctors, and researchers find products to some of the aforementioned issues. This review provides an justification of Machine Learning (ML) based on studies that are relevant. For the purpose of improving patient outcomes and alleviating healthcare costs, swift detection and diagnosis of medical issues like diabetes, chronic kidney disease, liver disease, and breast cancer are essential. Machine learning have grown into a viable method for screening for and detecting chronic illnesses in recent years. We want that will assist with early disease diagnosis and treatment by creating a machine learning model for various disease belonging through this project. A significant set of data of medical records, ranging from patient demographics, medical histories, symptoms, and test findings for a diagnostic will be used to train the model that has been suggested. So excellent data quality and completeness, the dataset will be meticulously selected and preprocessed. Relevant nuances that was demonstrated to affect illness risk, such as genetic markers and decisions regarding one's lifestyle, will also be were present. |
| Keywords: | Machine Learning, Decision tree, Adaboost , Xgboost and Catboost and ML techniques, evaluation |
| Cite Article: | "Mutiple Disease Detection using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h155-h165, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404720.pdf |
| Downloads: | 00031 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2404720 Registration ID: 219094 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: h155-h165 Country: Rajampet, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404720 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404720 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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