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首页> 外文期刊>International journal of enterprise network management >Comparative study of machine learning techniques for breast cancer identification/diagnosis
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Comparative study of machine learning techniques for breast cancer identification/diagnosis

机译:机器学习技术在乳腺癌识别/诊断中的比较研究

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摘要

The number of new cases of female breast cancer was 124.9 per 100,000 women per year. Similarly, deaths were 21.2 per 100,000 women per year. It calls for an urge to increase the awareness of breast cancer and very accurately analyse the causes which may differ in minute variations. This is why the application of computation techniques are widely increasing to support the diagnostic results. In this paper, we present the application of several machine learning techniques and models like neural network, SVM is used to quantify the classifications. The techniques that are most reliable, accurate and robust are emphasised. It gives a plethora of explorations into the research field for developing predictive models. To achieve higher reliability on the data, we present the comparison of various Machine Learning techniques on a dataset that is available on the website Kaggle.
机译:每年女性乳腺癌新发病例为每10万名女性124.9人。同样,每年每100,000名妇女中有21.2人死亡。它呼吁提高对乳腺癌的认识,并非常准确地分析可能因微小差异而引起差异的原因。这就是为什么计算技术的应用广泛增加以支持诊断结果的原因。在本文中,我们介绍了几种机器学习技术和模型(如神经网络,SVM用于量化分类)的应用。强调最可靠,准确和可靠的技术。它对开发预测模型的研究领域进行了大量探索。为了获得更高的数据可靠性,我们在Kaggle网站上提供的数据集中比较了各种机器学习技术。

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