Fuzzy Logic for Heart Disease Prediction

Muhammad Raihan Rusli; Normalisa

doi:10.36079/lamintang.ijai-01301.1057

Fuzzy Logic for Heart Disease Prediction

Muhammad Raihan Rusli
Normalisa

DOI: https://doi.org/10.36079/lamintang.ijai-01301.1057

Keywords: Clinical Decision Support System, Fuzzy Logic, Heart Disease Prediction, Medical Risk Assessment, Web Application

Abstract

Cardiovascular disease remains one of the leading causes of mortality worldwide, emphasizing the importance of early and accurate risk prediction systems. This study proposes a web-based heart disease prediction system utilizing a fuzzy logic approach to effectively manage uncertainty and imprecision in clinical data. As illustrated in the system interface, the model incorporates five primary input parameters: blood pressure, blood sugar level, cholesterol level, body mass index (BMI), and family history. These parameters are transformed into linguistic variables through fuzzification and evaluated using a rule-based fuzzy inference system constructed from expert knowledge and recent research findings. The fuzzy logic framework enables flexible decision-making that closely resembles human reasoning, overcoming limitations of conventional threshold-based diagnostic methods. The system produces an interpretable heart disease risk classification, such as low or high risk, which is presented through an interactive web interface along with a prediction history feature. Based on a review of related studies published within the last five years, fuzzy logic and neuro-fuzzy models have shown strong performance, transparency, and suitability for clinical decision support. The proposed system demonstrates the practicality of integrating fuzzy logic into web applications to support early heart disease risk assessment and preventive healthcare.

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Author Biographies

Muhammad Raihan Rusli

Computer Science Engineering, International University Liaison Indonesia. Banten, Indonesia.

Normalisa

Computer Science Engineering, International University Liaison Indonesia. Banten, Indonesia.

This is an open access article, licensed under CC-BY-SA

Published

2026-06-24

Downloads : 6

How to Cite

[1]

M. R. Rusli and Normalisa, “Fuzzy Logic for Heart Disease Prediction”, International Journal of Artificial Intelligence, vol. 13, no. 1, pp. 24-32, Jun. 2026.

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Vol 13 No 1: June 2026

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Articles

References

C. Indirani et al., “Heart Disease Prediction Using Fuzzy Logic-Based Image Processing and Classification Techniques,” Bioscan, vol. 20, no. 2, pp. 163–173, Jun. 2025. doi: 10.63001/tbs.2025.v20.i02.

M. F. Al Hakim, N. Fajriati, and R. N. Pratama, “Heart Disease Diagnosis Using Tsukamoto Fuzzy Method,” J. Adv. Inf. Syst. Technol., vol. 5, no. 1, pp. 45–54, May 2023. doi: 10.15294/jaist.v5i1.67565.

M. L. Ali, “Diagnosis of heart diseases: A fuzzy-logic-based approach,” PLoS One, vol. 19, no. 3, Art. no. e0298412, Mar. 2024. doi: 10.1371/journal.pone.0298412.

D D. Damodharan, A. K. Goel, K. K. Agrawal, S. Johri, and A. Kumar, “CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 4, pp. 191–199, Apr. 2023. doi: 10.17762/ijritcc.v11i4.6392.

S. Shafiq et al., “FuzzDL-HeartPredict: Heart Attack Risk Prediction Using Fuzzy Logic and Deep Learning,” ICCK Trans. Adv. Comput. Syst., vol. 1, no. 2, pp. 63–77, 2025. doi: 10.62762/TACS.2025.794425.

I. Puspita and A. M. Abadi, “Diagnosing Heart Disease using Wavelet Transformation and Adaptive Neuro Fuzzy Inference System (ANFIS) Based on Electrocardiagram (ECG),” EKSAKTA: J. Sci. Data Anal., vol. 19, no. 1, pp. 71–82, Feb. 2019. doi: 10.20885/eksakta.vol19.iss1.art7.

V. Febriani, D. Lestari, S. Mardiyati, and O. Lilyasari, “Fuzzy Logistic Regression Application on Predictions Coronary Heart Disease,” Barekeng: J. Math. Appl., vol. 17, no. 1, pp. 571–580, Mar. 2023. doi: 10.30598/barekengvol17iss1pp0571-0580.

A. El-Ibrahimi, “Fuzzy based system for coronary artery disease prediction,” Biomed. Signal Process. Control, vol. 99, Art. no. 106884, Jan. 2025. doi: 10.1016/j.bspc.2024.106884.

S. Sumarlinda, A. B. Rahmat, Z. B. A. Long, and W. Lestari, “The Improvement Prediction Model Using ANFIS for Medical Dataset,” J. Theor. Appl. Inf. Technol., vol. 102, no. 5, pp. 1663–1672, Mar. 2024.

O. I. Naibaho, “Cardiac Abnormality Detection Using Adaptive Neuro-Fuzzy Inference System,” J. Teknol. Dan Ilmu Komput. Prima (JUTIKOMP), vol. 8, no. 1, pp. 26–39, Apr. 2025. doi: 10.34012/jutikomp.v8i1.6999

D. Prabha Devi and C. Palanisamy, “Early heart disease prediction using LV-PSO and Fuzzy Inference Xception Convolution Neural Network on phonocardiogram signals,” Front. Neuroinform., vol. 19, Art. no. 1655003, 2025. doi: 10.3389/fninf.2025.1655003.

M. Salinas et al., “An Explainable Fuzzy Framework for Assessing Preeclampsia Classification,” Biomedicines, vol. 13, no. 6, Art. no. 1483, Jun. 2025. doi: 10.3390/biomedicines13061483

K. Navin and R. Srinivasan, “Fuzzy rule based classifier model for evidence based clinical decision support systems,” Biomed. Signal Process. Control, vol. 92, Art. no. 106044, Jun. 2024. doi: 10.1016/j.bspc.2024.106044.

M. S. Rahman, M. A. Khan, and S. M. A. Iqbal, “Development of a neural network-based adaptive fuzzy logic controller (ANFIS) for biomedical application,” Appl. Sci., vol. 15, no. 2, Art. no. 742, Jan. 2025. doi: 10.3390/app15020742.

S. Ramya and K. S. Kumar, “An attention infused multi-stage parallel adaptive neuro fuzzy inference system for complex medical data classification,” Soft Comput., vol. 29, no. 4, pp. 2481–2495, Feb. 2025. doi: 10.1007/s00500-024-09876-y.

M. Al-Mahasneh, M. Aljarrah, and T. Rababah, “Application of Hybrid Neural Fuzzy System (ANFIS) in Food Processing and Technology,” Food Eng. Rev., vol. 8, no. 3, pp. 351–366, Sep. 2016. doi: 10.1007/s12393-016-9141-7.

I. Wajid, M. A. Khan, and S. Nazir, “A hybrid model combining convolutional neural networks and temporal convolutional networks for biomedical signal forecasting,” Biomed. Signal Process. Control, vol. 101, Art. no. 107214, Jan. 2025. doi: 10.1016/j.bspc.2024.107214.

J. Sen and S. Bhattacharya, “An explainable hybrid framework for early detection of cardiovascular diseases using Categorical Boosting and Bees algorithm,” Sci. Rep., vol. 15, no. 1, Art. no. 45748, Dec. 2025. doi: 10.1038/s41598-025-28514-4.

H. C. Villamil and J. E. Camargo, “Fuzzy logic system for determining body mass index and nutritional status,” Diagnostics, vol. 13, no. 14, Art. no. 2345, Jul. 2023. doi: 10.3390/diagnostics13142345.

L. J. Muhammad, E. A. Algehans, and E. S. Al-Shamery, “Design of a fuzzy-based expert system for coronary artery disease diagnosis,” Health Technol., vol. 11, no. 2, pp. 315–327, Mar. 2021. doi: 10.1007/s12553-021-00522-8.

P. Kora, K. Swaraja, and M. P. R. S. Kiran, “Fuzzy rule-based clinical system for the automatic detection of coronary heart disease,” Biomed. Signal Process. Control, vol. 54, Art. no. 101618, Sep. 2019. doi: 10.1016/j.bspc.2019.101618.

X. H. Zhao, Y. Wang, and L. Zhang, “Aggregated deep learning models in ensemble and hybrid modalities for complex medical data analysis,” J. Med. Syst., vol. 49, no. 2, Art. no. 10245, Feb. 2025. doi: 10.1007/s10916-024-02145-w.

I. E. Ahmed, R. Mehdi, and E. A. Mohamed, “The role of artificial intelligence in developing a banking risk index: an application of adaptive neural Network-Based fuzzy inference system (ANFIS),” Artif. Intell. Rev., vol. 56, no. 11, pp. 13873–13895, Nov. 2023. doi: 10.1007/s10462-023-10486-w.

Y. Liu, “Discussion on the Enterprise Financial Risk Management Framework Based on AI Fintech,” Decis. Mak. Appl. Manag. Eng., vol. 7, no. 1, pp. 254–269, 2024. doi: 10.31181/dmame712024942.

E. Kuznetsova, “Adaptive Fuzzy Logic Integration Framework for Dynamic System Modeling,” J. Technol. Inform. Eng., vol. 4, no. 3, pp. 112–125, Dec. 2025. doi: 10.51903/jtie.v4i3.456.

Y. Farooqui, K. Shekokar, P. Bhatt, and K. Macwan, “Explainable Fuzzy Logic in AI: Enhancing Transparency and Trust in Deep Learning Models,” J. Mach. Comput., vol. 5, no. 4, pp. 2241–2253, Oct. 2025. doi: 10.53759/7669/jmc202505174.

N. Zacarias-Morales, J. C. Herrera-Lozada, and H. M. Perez-Meana, “Fuzzy Logic Classification System for Enhanced Decision-Making in Complex Environments,” Applied Sciences, vol. 15, no. 3, Art. no. 1245, Feb. 2025. doi: 10.3390/app15031245.

Z. Attarilar, M. R. Ebrahimi, and S. A. Ghorashi, “Adaptability of fuzzy logic systems in medical disease diagnosis: A comprehensive review,” J. Med. Syst., vol. 49, no. 1, Art. no. 10472, Jan. 2025. doi: 10.1007/s10916-024-02315-w.

Uma and J. Sharline, “Impact of fuzzy logic and its applications in medicine: A review,” Int. J. Stat. Appl. Math., vol. 7, no. 2, part A, pp. 12–18, 2022. doi: 10.22271/maths.2022.v7.i2a.789.

A. Suzuki, “Fuzzy Logic Systems for Healthcare Applications,” J. Biomed. Sci. Health. Appl., vol. 4, no. 1, pp. 1–15, Apr. 2024. doi: 10.53759/jbsha/JBSHA202404001.