We are developing this project with Dr Rana Zeeshan Haider as an external who is associated with NCIBD. He gives us the dataset of different patients which contains all CBC parameters. This dataset will be used for training the model in order to find Anemia and its common type. For deep understandin
Machine Learning based decipherment of Research CBC parameters in early discernment of common types of anemias
We are developing this project with Dr Rana Zeeshan Haider as an external who is associated with NCIBD. He gives us the dataset of different patients which contains all CBC parameters. This dataset will be used for training the model in order to find Anemia and its common type. For deep understanding we researched through different websites and research papers. Detailed overview of anemia i.e., it is a common nutritional deficiency disorder and there are several types and classification of anemia.
Common types of anemia include:
To help the health sector in the detection and discernment of common types of anemia in early stages to avoid severity of the disease in patients.
Using Machine Learning algorithm to identify and detect the common types of anemia at the early stage using Research CBC parameters instead of routine CBC to help the health sector to control this disease among the patients before it reaches a severe level
To assist the health sector to detect and identify anemia in early stages, along with its type using research CBC parameters instead of routine CBC. Doctors and research will have probabilistic approach about the presence/absence of disease along with its type without spending many time on research to obtain any conclusion.
Methodology used for the development of this software project is the Waterfall model. As this is a safetycritical system that addresses the health disease/disorder, its requirement must be clearly specified and gathered in great detail without any loophole left for the understanding of any point. Also, as this is a research-based project, its requirements and documentation must be clearly specified.
The final product that will be delivered will consisit of the following parts:
FRONTEND:
BACKEND:
API:
More about final deliverable:
The finalized product is web based application that allow end-user to upload the CBC parameters in CSV file. The data is then send to the trained Machine Learning model, which draw some conclusion from the given data. After that it provide the results in the form of visuals, accuracy of output, Fscore of output, comparison of possibilities of different kind of anemia as predicted according to the given data.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Nvidia RTX 2080 | Equipment | 1 | 65000 | 65000 |
| Total in (Rs) | 65000 |
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