| Popis: |
The search for quick detection and solution of Lung cancer is traced back to the Stone Age by medical experts and health workers. Often time, patients are confronted with the challenges of running through different medical outfits and spending a lot of money to fight the deadly disease and still ended up in their untimely grave. This is not because the medical experts could not handle the patient appropriately, but because the disease has gotten to the final or terminal stage, and at this point it is irreversible. The quest to reduce the challenges faced by patients and medical experts to get a timely solution to the diagnosis or detection of lung cancer in other to save the life of the patient has motivated this research. The work aims at designing and implementing an intelligent-based system for the diagnosis of lung cancer driven by fuzzy c-means algorithm.The methodology used was Object-Oriente Analysis and Design (OOAD). The fuzzy model was designed in three main stages includingfuzzification, FCM inference system, and defuzzification. The triangular membership (Tmf) function was used to map the input parameters to the output parameter. The FIS model was designed using Mamdani’s inference mechanism. C# programming language was used on a windows 10 platform to integrate the Matlab FIS output for better performance and MySQL database 5.7.14 from WAMP server 3.0.6 was used as the back-end engine. To validate this work, data was captured directly from the 50 volunteers. The training, testing, and checking FCM KMSI values of 0.00123, 0.03123, and 0.06301 respectively were observed in the process at 150 epochs and an average error of 0.0002102 was observed at 300 epochs. |
