Matthew Cobbinah

Work place: College of Science, Computer Science Department, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi- Ghana



Research Interests: Machine Learning, Intelligent Systems


Matthew Cobbinah holds B.Sc. Information and Communication Technology from Christ Apostolic University College, Kumasi Ghana (2017) and also MPhil Information Technology from Kwame Nkrumah University of Science and Technology (KNUST), Kumasi Ghana (2021). He currently works as a Lecturer at Christ Apostolic University College, Ghana. His research areas include, Artificial Intelligent Systems, Machine Learning, Soft Computing, Computer Application Technology and Information Technology.

Author Articles
Adaptive Neuro-Fuzzy Inferential Approach for the Diagnosis of Prostate Diseases

By Matthew Cobbinah Umar Farouk Ibn Abdulrahman Abaidoo Kwame Emmanuel

DOI:, Pub. Date: 8 Feb. 2022

In this study, Adaptive Neuro-fuzzy Inferential System (ANFIS) is adapted for diagnosing prostate diseases. The system involves generating and tuning a fuzzy inference system to handle the imprecise terms used for describing prostate cases and severity. Several diagnostic variables were used to learn the feature statistics present in a typical data, while the trained model was validated and adapted for testing new prostate cases. A total of 335 data from patients’ records were collected at the Medi Moses Prostate Centre, Kumasi Ghana. The dataset was partitioned into 70% which was used for model training, and the other 30% was utilized in the validation phase. The proposed model was implemented in the MATLAB environment. Evaluation result from the proposed system demonstrated that the system achieved an accurate diagnostic result with an RMSE value of 11%. This indicates that the system has a relatively high accuracy and could be accepted for prostate diagnosis. Furthermore, the model was able to learn well and generalize the features in the data set, making the proposed ANFIS model suitable for new cases. Performance analysis showed that the ANFIS is well suited for handling the crispy values used in prostate diagnosis; thus, it can be extensively employed in other similar areas of medical diagnosis.

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