Cancer Prediction System
1.Background/ Problem Statement
As per the data provided by WHO (https://www.who.int/health-topics/cancer#tab=tab_1) 9.6 million people are estimated to have died worldwide due to cancer in 2018. Also, 3 lakh new cancer cases diagnosed each year are among children aged 0 – 19 years. Cancer is amongst the deadliest disease that a human can get affected with. However, the positive side to it is that if the cancer is detected at an early stage, then about 50% of cancers can be prevented & cured. Otherwise, it may lead to a very critical situation and may even cause death. Hence, this makes it even more necessary to have a system or technology that can help doctors detect cancer at an early stage where it can be treated effectively.
To solve this problem using advanced technological solutions & artificial intelligence, we have come up with a Cancer Prediction System using the Naïve Bayes Machine Learning algorithm. This system takes a statistical approach by employing probabilistic & optimization techniques to draw out a result based on past datasets. This evaluation technique aims at helping doctors & pathologists to detect cancer at an early stage where it can be prevented & cured, thereby saving many lives.
- Working of the Project
In this system, users to get instant guidance on their Cancer disease through an intelligent system online. The Cancer Disease Prediction web application is an end user support and online consultation project. The application is fed with various details and the Cancer disease associated with those details. It allows user to share their Cancer related issues. It then processes user specific details to check for various cancer disease that could be associated with the inputs received from user. Here we use Naïve Bayes algorithm to predict the most accurate cancer disease that could be associated with user’s details. Based on result, system automatically shows the result specific doctors for further treatment. The system allows user to view doctor’s details.
- Advantages
- User can easily get the cancer disease prediction on single click
- Based on predicted results, system will display relevant doctor details for further communications
- User can view doctor details at any point of time
- System is kept online in order to serve people 24×7
- Algorithm
- Naïve Bayes Machine Learning
- System Description
The system comprises of 2 major entities with their modules as follows:
- Admin
- Login: Admin need to authenticate using login id and pass in order to access the system.
- Add/View Training Data: A relevant training set is to be filled by admin for the algorithm to analyse and predict results.
- Add/View Doctor Details: A specialist details with respect to cancer need to be added by admin in the system.
- View User Details: All the registered users are displayed to the admin.
- View Feedback: System related feedbacks are received from the registered users.
- User
- Register: In order to access the system, user need to register with basic details like Name, email, contact no., age, sex, etc.
- Predict Cancer (By providing Details like Age, Gender, blood clots in the urine, Urination visit in a Day, Chest pain, Coughing up blood, Pain/Itching in the mouth, Memory problems
- System will accordingly view Doctor to consult.
- Give Feedback: User will provide feedback regarding the system.
- View Doctor: User can view various doctor based on the predicted cancer.
- Project Life Cycle
The waterfall model is a classical model used in system development life cycle to create a system with a linear and sequential approach. It is termed as waterfall because the model develops systematically from one phase to another in downward fashion. The waterfall approach does not define the process to go back to the previous phase to handle changes in requirement. The waterfall approach is the earliest approach that was used for software development
- System Requirements
- Hardware Requirement
- Laptop or PC
- i3 processor system or higher
- 4 GB RAM or higher
- 100 GB ROM or higher
- Software Requirement
- Laptop or PC
- Language: Asp.Net C#
- Windows 7 or higher
- Visual Studio
- SQL Management Studio
- Limitation/Disadvantages
- The system is not fully automated, it needs data from user for full diagnosis
- Wrong inputs will affect the project outputs.
- Relevant cancer disease doctors will only be displayed if added by admin.
- Training data needs to be accurate in order to get prediction.
- Application – This system can be used naïve people in order to detect cancer.
- Reference