  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.
