Immunity Finder

Human Immunity System is a complex network of cells and proteins that defends the body against infection. Abnormalities of the immune system can lead to allergic diseases, immunodeficiencies and autoimmune disorders. Many people around the world suffer or die due to the affect of diseases with out having knowledge about their immune system. This program is aimed to match human T cells information with diseases T cells data. It is designed using (RNN) Recurrent Neural Networks algorithm with multiple steps and layers. In the first step we run K-means algorithm which is unsupervised learning algorithm on the T cells count and types of T-cells from the blood test results. This logic helps to map the T cell receptor sequencing data and convert them into a cluster by functional similarity. Second step run the multiclass classification algorithm to compare the person’s T cells cluster data with that person’s heredity diseases related cells data. Finally third step logic runs the multiclass classification algorithm to compare the person’s T cells cluster data with the most common diseases in that geographic area where he is living. Overall the results helps doctors and medical diagnostic centers to serve their patients with proactive health suggestions to boost their immunity and help from diseases.

Built With

• ai
• azure
• database
• javascript
• machine-learning