International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
IDENTIFYING EARLY WARNING SIGNS: PREDICTING CANCER SYMPTOMS THROUGH GENETIC ANALYSIS 1Ramalingam Sakthivelan NMK, 2Pradeep P, 3Prem B, 4Vishal S, 1 Associate Professor, Department of Computer Science and Engineering, 2,3,4 Student, Department of Computer Science and Engineering,
Paavai Engineering College (Autonomous), Pachal, Namakkal, Tamilnadu, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract Cancer remains one of the leading causes of
Key Words: K-Nearest Neighbors (KNN), Random Forest, Neural Networks
death worldwide, with early detection being crucial for improving patient outcomes. This project proposes a machine learning-based system that aims to predict cancer risk by analyzing the relationship between genetic markers and vitamin deficiencies. By integrating data from multiple sources, including The Cancer Genome Atlas (TCGA), NHANES (National Health and Nutrition Examination Survey), and SEER (Surveillance, Epidemiology, and End Results), the system seeks to identify patterns correlating genetic predispositions and vitamin levels with cancer incidence. The system employs advanced machine learning algorithms such as K-Nearest Neighbors (KNN), Random Forest, and Neural Networks to build predictive models capable of generating personalized cancer risk scores. These scores provide real-time alerts for individuals at higher risk, allowing for proactive intervention and preventive measures. A user-friendly interface is designed for healthcare providers, offering tools such as interactive dashboards, personalized reports, and visualizations of genetic and vitamin data, aiding in efficient patient risk monitoring and tailored health recommendations. Moreover, the system ensures data security by incorporating encryption and role-based access control, ensuring compliance with healthcare regulations such as HIPAA. The proposed system is scalable and adaptable, capable of handling large datasets and accommodating multiple cancer types. By enhancing early detection and enabling personalized prevention strategies, this system aims to make a significant impact on public health and reduce the burden of cancer-related mortality. This version provides a more detailed explanation of the project’s purpose, methodology, and anticipated impact, while still being concise and focused on key points. This study utilizes robust data integration techniques, ensuring comprehensive analysis and precise insights. Extensive validation against diverse datasets highlights the system's generalizability, while the userfriendly interface enables real-time decision-making support. The integration of advanced encryption mechanisms further ensures that patient confidentiality and data integrity are maintained.
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1.INTRODUCTION The integration of machine learning (ML) in the field of cancer prediction and diagnosis has witnessed remarkable advancements in recent years. Various studies have demonstrated that ML algorithms, such as K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), and Neural Networks (NN), can effectively classify cancer types, predict risk, and assist in early detection. One of the major areas of research has focused on the relationship between genetic mutations and cancer susceptibility, as genetic factors play a critical role in individual cancer risk. By analyzing genetic markers, researchers have been able to predict cancer on set and progression with a high degree of accuracy. Additionally, recent studies have explored the connection between vitamin deficiencies and cancer risk, suggesting that deficiencies in key vitamins may disrupt cellular processes and increase the likelihood of cancer development. The use of ML techniques in this context provides an innovative approach to cancer risk prediction, as large datasets comprising genetic markers and vitamin levels can be analyzed to identify complex patterns that are not easily discernible through traditional methods. In this project, the objective is to leverage ML algorithms such as KNN and Random Forest to analyze comprehensive data on vitamin levels, genetic markers, and cancer incidence. By developing predictive models, this research aims to identify individuals at a higher risk of cancer based on their genetic and vitamin profiles, enabling early detection and personalized prevention strategies. The existing literature highlights the potential of ML in enhancing cancer screening and improving patient outcomes through early intervention, but it also emphasizes the need for further research in integrating genetic and nutritional factors into predictive models for cancer risk assessment. Overall, this project builds upon previous findings that demonstrate the effectiveness of ML in cancer prediction, while introducing a novel focus on the interplay between genetic predispositions and vitamin deficiencies. By
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