Performance Assessment of Fuzzy Inference System on Medical Data

Fuzzy sets have been used in the medical field where uncertainty is prevalent. Medicine, which frequently straddles the line between science and art, is an excellent example of a field where ambiguity, hesitation, linguistic uncertainty, evaluation inaccuracy, natural diversity and subjectivity are prominent. Medical diagnosis is a complex problem that requires all of a person’s abilities, including intuition and the subconscious. A fuzzy inference system is a linguistic framework that can be used to model human thought processes. An extensive study is conducted in this investigation through evaluation of fuzzy inference system. The datasets used in this investigation are PIMA Indian diabetic dataset, Wisconsin breast cancer dataset, Parkinson disease dataset, and heart attack analysis dataset. These datasets were downloaded from Kaggle, world’s largest data science community. In this research work, the dataset used was reduced to fewer dimensions using domain knowledge of the particular disease. Using this transformed data, a fuzzy inference system is established, and the performance of the system is evaluated using performance evaluation measures such as precision, recall, F1-score, and specificity. The performance assessment of the Fuzzy Inference System on the medical datasets showcases promising results.