An AI Based Support System For The Diagnosis Of Breast Cancer

Debabrata Swain, Utsav Mehta, Ayush Bhatt, Ashwini Ramanuj
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Abstract

At present, the health-care system is facing a drastic increase in the number of cancer patients. The huge number of Breast cancer cases among women and its constant increase is a matter of concern for the clinical support systems. Early diagnosis plays a crucial role in the treatment of such a fatal ailment. The late diagnosis is the main cause of the death of many people around the globe. Also, Image processing techniques do exist for the prediction of breast cancer however the scope of misdiagnosis still exists in this technique. For this reason, timely and accurate screening of breast cancer is a major challenge for the clinical support system. In such cases, machine learning can be used as an effective tool to reduce uncertainty in clinical decision-making. Machine learning is the technique for making the machine capable through a large amount of data to perform learning and produce useful outputs. In this work, a machine learning based classifier is developed using the Support vector machine for the diagnosis of breast cancer illness. The clinical data used for the creation and validation of the model is obtained from the UCI repository. SMOTE based oversampling has been performed to balance the classes of malignant and benign tumors in the dataset. A set of seven important features were selected based on their f-value to reduce the time as well as the cost of medical examination. The proposed classifier has reported a testing accuracy of 98.32%.
基于人工智能的乳腺癌诊断支持系统
目前,医疗保健系统正面临癌症患者数量急剧增加的问题。女性乳腺癌病例的巨大数量及其持续增长是临床支持系统关注的问题。早期诊断在治疗这种致命疾病中起着至关重要的作用。晚期诊断是全球许多人死亡的主要原因。此外,图像处理技术确实可以用于乳腺癌的预测,但该技术仍然存在误诊的范围。因此,及时准确地筛查乳腺癌是临床支持系统面临的主要挑战。在这种情况下,机器学习可以作为一种有效的工具来减少临床决策中的不确定性。机器学习是一种使机器能够通过大量数据进行学习并产生有用输出的技术。在这项工作中,使用支持向量机开发了一个基于机器学习的分类器,用于乳腺癌疾病的诊断。用于创建和验证模型的临床数据是从UCI存储库获得的。基于SMOTE的过采样已被执行,以平衡数据集中的恶性和良性肿瘤的类别。根据其f值选择了一组7个重要特征,以减少医学检查的时间和费用。该分类器的测试准确率为98.32%。
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