Fuzzy Logic
Python fuzzy inference system for breast-cancer tumour classification on the Wisconsin dataset. Beat neural nets, decision trees, and random forests.
Breast Cancer Tumour Classification With Fuzzy Inference System (FIS)
Of all cancers occurring in women, breast cancer is the most common, with 2 million new cases and over 0.5 million deaths globally each year. Many doctors are overwhelmed, having to deal with up to 70 patients a day. Mistakes in diagnosis can have deadly consequences.
This study presents a Python-based fuzzy logic knowledge-based inference system for the binary classification of breast cancer tumours, taking an extensive data-driven, supervised learning approach in defining linguistic terms, implication rule sets and membership functions, with the Wisconsin Diagnostic Breast Cancer dataset used as the empirical, impartial problem-domain expert. As a Python-based FIS can be deployed on cost-effective, mobile hardware including Raspberry Pi, it is extremely affordable, portable and readily located in any clinical environment.
A large set of configuration variants including five defuzzification methods are tested, prioritising the classification accuracy of malignant tumours. The best FIS model classified all test set malignant tumours correctly with an overall accuracy of 93.6%, outperforming logistic regression, decision tree, random forest and neural network machine learning methods.
Artifacts
Fuzzy Logic Theory
Theoretical assignment exploring the foundations of fuzzy set theory and inference systems.
Artifacts
- Theoretical assignment - grade 90%