According to GHO (Global Health Observatory (GHO), the high prevalence of a large variety of diseases such as Ischaemic heart disease, stroke, lung cancer disease and lower respiratory infections have remained the top killers during the past decade. The growth in the number of mortalities caused by these disease is due to the very delayed symptoms'detection. Since in the early stages, the symptoms are insignificant and similar to those of benign diseases (e.g. the flu ), and we can only detect the disease at an advanced stage. In addition, The high frequency of improper practices that are harmful to health, the hereditary factors, and the stressful living conditions can increase the death rates. Many researches dealt with these fatal disease, and most of them applied advantage machine learning models to deal with image diagnosis. However the drawback is that imagery permit only to detect disease at a very delayed stage and then patient can hardly be saved. In this Paper we present our new approach "DeepLCP" to predict fatal diseases that threaten people's lives. It's mainly based on raw and heterogeneous data of the concerned (or under-tested) person. "DeepLCP" results of a combination combination of the Natural Language Processing (NLP) and the deep learning paradigm.The experimental results of the proposed model in the case of Lung cancer prediction have approved high accuracy and a low loss data rate during the validation of the disease prediction.

中文翻译：

---

用于疾病预测的通用 DeepLCP 模型：肺癌案例

然而缺点是图像只能在很晚的阶段检测到疾病，然后很难挽救患者。在本文中，我们展示了我们的新方法“DeepLCP”来预测威胁人们生命的致命疾病。它主要基于相关（或接受测试的）人员的原始和异构数据。自然语言处理（NLP）和深度学习范式结合的“DeepLCP”结果。所提出的模型在肺癌预测情况下的实验结果在验证过程中证明了高精度和低丢失数据率疾病预测。预测威胁人们生命的致命疾病。它主要基于相关（或接受测试的）人员的原始和异构数据。自然语言处理（NLP）和深度学习范式结合的“DeepLCP”结果。所提出的模型在肺癌预测情况下的实验结果在验证过程中证明了高精度和低丢失数据率疾病预测。预测威胁人们生命的致命疾病。它主要基于相关（或接受测试的）人员的原始和异构数据。自然语言处理（NLP）和深度学习范式结合的“DeepLCP”结果。所提出的模型在肺癌预测情况下的实验结果在验证过程中证明了高精度和低丢失数据率疾病预测。