IMPLEMENTATION OF FUZZY INFERENCE SYSTEM (FIS) FOR CARDIOVASCULAR DISEASES PREDICTION
DOI:
https://doi.org/10.47701/icohetech.v4i1.3418Keywords:
Fuzzy Inference System, Membership Function, Mamdani FIS, Prediction, cardiovascularAbstract
Abstract- Cardiovascular diseases (CVDs) continue to be a leading cause of mortality worldwide. Early and accurate prediction of CVDs risk is crucial for effective prevention and management. This study presents the implementation of a Fuzzy Inference System (FIS) for predicting suseptibility cardiovascular diseases. The implementation of FIS for the prediction of cardiovascular disease is by determining the membership function for risk factors that influence the susceptibility of the disease. The FIS developed in this study integrates five risk factors, including age, systolic blood pressure, diastolic blood pressure, blood sugar and cholesterol and one output parameter CVDs prediction. The FIS method used Mamdani with 162 rules. Real-world patient data diagnosed with cardiovascular disease is used to train and validate the FIS. Validity testing produces 100% valid data. Testing is carried out using patient data. The method used to validate the results of the FIS implementation is by distributing questionnaires to several paramedics.. These findings provide insights into further refinements of CVD risk modeling and potential applications in clinical practice.
References
Antar, R. K., ALotaibi, S. T., & AlGhamdi, M. (2021). Heart Attack Prediction using Neural Network and Different Online Learning Methods. International Journal of Computer Science & Network Security, 21(6), 77–88.
Dehdar Karsidani, S., Farhadian, M., Mahjub, H., & Mozayanimonfared, A. (2022). Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model. BMC Cardiovascular Disorders, 22(1), 1–8. https://doi.org/10.1186/s12872-022-02825-0
Espitia, H., Soriano, J., Machón, I., & López, H. (2019). Design methodology for the implementation of fuzzy inference systems based on boolean relations. Electronics (Switzerland), 8(11), 1–28. https://doi.org/10.3390/electronics8111243
Faris, M., Hakim, A., Fajriati, N., & Pratama, R. N. (2023). Heart Disease Diagnosis Using Tsukamoto Fuzzy Method. Journal of Advances in Information Systems and Technology, 5(1). https://journal.unnes.ac.id/sju/index.php/jaist
Feng, J., Wang, Q., & Li, N. (2021). An intelligent system for heart disease prediction using adaptive neuro-fuzzy inference systems and genetic algorithm. Journal of Physics: Conference Series, 2010(1). https://doi.org/10.1088/1742-6596/2010/1/012172
Gowda, C. N., & Chaithra, N. (2020). Classification and Regression Tree (CART) Algorithm for the Prediction of Ischemic Heart Disease. International Journal of Future Generation Communication and Networking, 13(4), 196–206. https://www.researchgate.net/publication/344469894
Harjai, S., & Khatri, S. K. (2019). An Intelligent Clinical Decision Support System Based on Artificial Neural Network for Early Diagnosis of Cardiovascular Diseases in Rural Areas. Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 729–736. https://doi.org/10.1109/AICAI.2019.8701237
Husein, A. M., & Simarmata, A. M. (2019). Drug Demand Prediction Model Using Adaptive Neuro Fuzzy Inference System (ANFIS). SinkrOn, 4(1), 136. https://doi.org/10.33395/sinkron.v4i1.10238
Liu, J., Dong, X., Zhao, H., & Tian, Y. (2022). Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion. Processes, 10(4). https://doi.org/10.3390/pr10040749
Mobispc, C. C., Adda, M., Bouzouane, A., Ibrahim, H., Moshawrab, M., & Adda, M. (2022). ScienceDirect Cardiovascular Cardiovascular Events Events Prediction Prediction using using Artificial Artificial Intelligence Intelligence Models and Rate Variability Models and Heart Rate Variability. Procedia Computer Science, 203(2019), 231–238. https://doi.org/10.1016/j.procs.2022.07.030
Naseer, I., Khan, B. S., Saqib, S., Tahir, S. N., Tariq, S., & Akhter, M. S. (2020). Diagnosis Heart Disease Using Mamdani FuzzyInference Expert System. EAI Endorsed Transactions on Scalable Information Systems, 7(26), 1–9. https://doi.org/10.4108/eai.15-1-2020.162736
Nguyen, K. L., Ghosh, R. M., Griffin, L. M., Yoshida, T., Bedayat, A., Rigsby, C. K., Fogel, M. A., Whitehead, K. K., Hu, P., & Finn, J. P. (2021). Four-dimensional Multiphase Steady-State MRI with Ferumoxytol Enhancement: Early Multicenter Feasibility in Pediatric Congenital Heart Disease. Radiology, 300(1), 162–173. https://doi.org/10.1148/radiol.2021203696
Olsson, A., & Nordlöf, D. (2015). Early screening diagnostic aid for heart disease using data mining AN EVALUATION USING PATIENT DATA THAT CAN BE OBTAINED WITHOUT MEDICAL EQUIPMENT.
Ravi, R. (2022). Prediction of Cardiovascular Disease using Machine Learning Algorithms. Ciees, 24–26.
Rizvi, S., Mitchell, J., Razaque, A., Rizvi, M. R., & Williams, I. (2020). A fuzzy inference system (FIS) to evaluate the security readiness of cloud service providers. Journal of Cloud Computing, 9(1). https://doi.org/10.1186/s13677-020-00192-9
Sivagowry S. (2015a). An Intelligent System based on Fuzzy Inference System to prophesy the brutality of Cardio Vascular Disease. www.ACSIJ.org
Sivagowry S. (2015b). An Intelligent System based on Fuzzy Inference System to prophesy the brutality of Cardio Vascular Disease. www.ACSIJ.org
