用机器学习+位置服务提升泰伦加纳邦癌症筛查率
Devising a solution to the problems of Cancer awareness in Telangana
- 基于人口统计学特征构建乳腺/宫颈癌风险预测模型
- 仅3.3%女性接受宫颈癌筛查，模型助力早期识别
- 结合定位与健康卡，推动精准医疗和科普宣传
2020年，泰伦加纳邦仅有3.3%的女性接受宫颈癌筛查，0.3%接受乳腺癌筛查，2.3%接受口腔癌筛查。尽管早期发现是降低发病率和死亡率的关键，但公众对乳腺癌和宫颈癌的症状及筛查认知极低。为此，我们开发了基于决策树分类器的宫颈癌风险预测模型和基于支持向量分类器的乳腺癌风险预测模型，可依据人口统计特征判断个体患病风险。系统还可根据用户位置推荐最近的医院或癌症治疗中心，并集成电子健康卡以记录个人医疗信息，支持开展针对性健康宣教活动。该方案有望显著提升癌症筛查覆盖率和公众认知水平。
原文摘要 · Abstract (English)
According to the data, the percent of women who underwent screening for cervical cancer, breast and oral cancer in Telangana in the year 2020 was 3.3 percent, 0.3 percent and 2.3 percent respectively. Although early detection is the only way to reduce morbidity and mortality, people have very low awareness about cervical and breast cancer signs and symptoms and screening practices. We developed an ML classification model to predict if a person is susceptible to breast or cervical cancer based on demographic factors. We devised a system to provide suggestions for the nearest hospital or Cancer treatment centres based on the users location or address. In addition to this, we can integrate the health card to maintain medical records of all individuals and conduct awareness drives and campaigns. For ML classification models, we used decision tree classification and support vector classification algorithms for cervical cancer susceptibility and breast cancer susceptibility respectively. Thus, by devising this solution we come one step closer to our goal which is spreading cancer awareness, thereby, decreasing the cancer mortality and increasing cancer literacy among the people of Telangana.
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