The problem Diagno Plus+ solves

We are trying to solve the problems which is faced by a lot of people in today’s world i.e. DISEASE DIAGNOSIS. . Diagnostic error is one of the most important safety problems in health care today, and inflicts the most harm. According to a research lakhs of patients die annually in the India alone, from diagnostic errors. Diagnostic problems include:
• Wrong labeling of samples
• Clerical mistakes
• Improper performance of the tests
• Instruments failure
• Mistakes in the calculation of the result
• Delayed performance of the tests
• Expensive diagnostics tests
• Whole process is time consuming and requires traveling costs
• These services are not available for people living in remote areas
• Creates a lot of biomedical wastes, etc.

Nowadays, patients receive care from numerous medical specialists, each of whom orders a separate set of tests and interprets them from their own unique point of view.
So there is a need of to diagnose diseases through some other approaches, hence lowering doctor's burden and boosting the efficiency of illness detection.

All the collected data (user’s detail and medical history) will be updated to the real-time database. Only Medical Authorities would be authorized to that database since it will be having sensitive information regarding every patient that may or may not be relevant to one user in specific and this data can be used by medical authorities to develop medicines and medical equipments. Therefore, it will help to create a complete ecosystem that will foster the treatment process of the patient. Hence, this will help to uplift the entire medical infrastructure. This approach is cost effective as it saves all the transportation charges and all the money that a patient spends on different types of lab tests. This code would keep in environment friendly diagnosis as it will reduce the carbon footprints by keeping all the data in digital format and removing the need of physical copy of the X-rays.

Challenges we ran into

1. collection of data set for different diseases.
2. data preporcessing
3. making predictive models to achieve high accuracy.
4. desinging website and making it live.
5. final code integration with the website.