Throat cancer Detection using machine learning

Registration ID: IJNRD_226244

Published ID: IJNRD2408035

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I. Venkata Neeraja

Abstract, introduction ,computational approach ,existing system, literature survey ,system design, architecture ,methodology and algorithm ,results discussion comparisons conclusion references

ABSTRACT Among the various types of diseases, cancer is considered as one of the deadly diseases in the world. In order to Overcome our research work includes data collection which is further analyzed and modelled using machine learning techniques Moreover, Machine learning models were evaluated as well as compared based on performance metrics parameters like Accuracy, Precision, Recall, F1score. Medical applications in Machine Learning (ML) algorithms well- being state on analyzing of the different attributes that have a high impact on getting illness. Cancer is one among of the human disease where researchers are still struggling for the complete curing. Cancer is the heterogeneous disease and its treatment varies from one type to and can inculcate different phases. Throat cancer is a tumor that spreads throughout the voice box(larynx), tonsils, or the throat(pharynx). In the initial stage, it is actively recommended to diagnose throat cancer and also get the proper medication. Machine learning techniques are used to effectively detect the throat cancer and specifically for the supervised learning classification algorithms. Throat cancer, a significant global health concern, requires early detection for effective treatment and improved patient outcomes. Detecting throat cancer using machine learning involves several steps., including data collection, data preprocessing, feature extraction, model training and also evaluation. Data collection involves gathering diverse medical records including symptoms, medical history, and diagnostic test results, to form a comprehensive dataset. Preprocessing techniques are applied to clean the data and prepare it for analysis. Feature extraction is conducted to identify relevant features that distinguish between cancerous and noncancerous cases. Several Machine learning algorithms, including logistic regression, support vector machines, random forest, and k nearest neigh bours. The models are trained on a portion of the dataset and evaluated using various performance metrics such as accuracy, precision, recall and f1score.Hyper parameter tuning and cross validation are employed to optimize model performance and ensure robustness.

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I. Venkata Neeraja (August-2024). Throat cancer Detection using machine learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(8), a369-a399. https://ijnrd.org/papers/IJNRD2408035.pdf

Volume 9 Issue 8, August-2024

Pages : a369-a399

Paper Reg. ID: IJNRD_226244

Published Paper Id: IJNRD2408035

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Srikakulam , Andhra pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2408035.pdf

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