International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 07 | July 2024
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p-ISSN: 2395-0072
PREDICTION OF OVARIAN CANCER USING EXPLAINABLE AI: A REVIEW Vidyasre N 1, Manasa T P 2 1 Student, Bangalore Institute of Technology, Karnataka, India
2 Assistant Professor, Dept. of Computer Science & Engineering, Bangalore Institute of Technology, Karnataka,
India ----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The prevention and early detection of ovarian
providing a foundation for future advancements in the detection and management of ovarian cancer.
cancer are critical for improving women's health and wellbeing. Ovarian cancer, often termed the "silent killer," typically presents without apparent symptoms in its early stages, making early detection challenging and significantly reducing the chances of successful treatment and survival. Machine Learning (ML) techniques offer powerful tools for identifying complex patterns in biomarkers, surpassing conventional diagnostic methods in accuracy. However, these advanced algorithms often lack transparency, making it difficult to gain physical insights into the diagnostic process. To address this, integrating eXplainable Artificial Intelligence (XAI) can enhance our understanding of how ML models make decisions, providing valuable insights into the diagnostic process. This paper aims to showcase best practices for combining XAI and ML methods in biomarker validation applications, ultimately improving early detection and treatment outcomes for ovarian cancer.
2. FLOW DIAGRAM A flowchart visually represents a process, using standardized symbols to illustrate steps, decisions, and flow of information. It serves to clarify processes, improve understanding, and facilitate communication in various fields, from engineering to business and healthcare.
Key Words: Ovarian Cancer, Machine Learning Algorithms, eXplainable Artificial Intelligence (XAI), Biomarker Validation, Early Detection.
1. INTRODUCTION Ovarian cancer often referred to as the "silent killer," poses a significant threat to women's health due to its typically asymptomatic early stages, which frequently result in delayed diagnosis and decreased treatment effectiveness. The undetected nature of this disease underscores the urgent need for improved detection techniques. Early detection is paramount for enhancing treatment effectiveness and achieving better patient outcomes. To address this critical issue, our study proposes a novel approach for predicting ovarian cancer. This study provides a comprehensive analysis of the development and evaluation process of the proposed model. By leveraging the strengths of both boosting and bagging techniques, our approach overcomes the limitations of individual classifiers. Additionally, the interpretability of the model is enhanced through the application of eXplainable AI (XAI) techniques, such as SHapley Additive Explanations (SHAP), which offer valuable insights into the risk factors for ovarian cancer. This study aims to advance the primary objective of improving patient outcomes and reducing ovarian cancer-related mortality rates. The subsequent sections delve into the methodology, results, and implications of our approach,
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The flow chart diagram outlines the process of predicting and classifying ovarian cancer using machine learning techniques, starting with the initiation of the workflow. The first step involves uploading the ovarian cancer dataset, followed by data preprocessing, where the raw data is cleaned and prepared for analysis. Next, feature extraction
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