Fuzzy Logic: Mastering Uncertainty Across Domains

Isabella Martínez^*
*Correspondence: Isabella Martínez, Department of Environmental Engineering,, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico, Mexico, Email:

Introduction

One study introduces a new fuzzy logic system designed for predicting cardiovascular disease in its early stages. This system uses fuzzy inference rules to effectively manage the inherent uncertainty and imprecision commonly found in medical data, thereby aiming to provide diagnostic support that is both more accurate and timely than conventional methods [1].

Another study presents an adaptive fuzzy control scheme specifically for robotic manipulators. This scheme tackles significant challenges like output constraints and potential actuator faults. By employing fuzzy logic, the system approximates unknown nonlinearities and dynamically adapts to handle limitations in control inputs and unexpected system failures. This approach ultimately enhances the operational reliability and overall performance of robot operations [2].

An article introduces an innovative adaptive fuzzy logic energy management system developed for hybrid electric vehicles. This system’s core function is to dynamically optimize the power flow between various energy sources within the vehicle. This optimization leads to improved fuel efficiency and a reduction in emissions, as the system intelligently adapts to real-time driving conditions and the driver's specific behavior [3].

A study develops a fuzzy logic-based decision support system to assist in the diagnosis and severity assessment of COVID-19. This system integrates medical knowledge and patient symptoms as fuzzy sets, which allows for a more nuanced and interpretable evaluation. This capability is crucial in helping clinicians make faster and more informed decisions, especially in critical situations [4].

Research proposes an enhanced energy management system for standalone hybrid microgrids. This system integrates solar PhotoVoltaic (PV), wind turbines, and battery storage, all controlled by a fuzzy logic controller. Its primary aim is to effectively balance power generation and demand, thereby improving the stability and efficiency of renewable energy systems operating in isolated environments [5].

Another research introduces a hybrid system that combines Fuzzy Cognitive Maps (FCMs), genetic algorithms, and deep neural networks for diagnosing depression. The fuzzy component in this system plays a key role by modeling complex, uncertain relationships between various symptoms. This integration significantly enhances both the interpretability and accuracy of the diagnostic process for depression [6].

A study proposes a fuzzy logic system integrated with machine learning for predicting diabetes mellitus. This fuzzy logic component is crucial for handling the inherent vagueness and uncertainty often present in diverse medical datasets. The system is designed to enhance predictive accuracy and offer more transparent reasoning for early diabetes detection, facilitating better health outcomes [7].

One paper introduces a fuzzy logic-based system for the early diagnosis of Parkinson's disease. This innovative system uses speech features as its primary input. It excels at managing the inherent imprecision often found in vocal biomarkers, offering an early and non-invasive diagnostic tool that can significantly contribute to improved patient outcomes through timely intervention [8].

A paper develops a fuzzy logic-based decision support system specifically for the early prediction of sepsis in Intensive Care Units. This system processes various physiological parameters using fuzzy rules. Its design helps identify high-risk patients earlier, allowing for more timely interventions and potentially improving survival rates for this critical medical condition [9].

A comprehensive review explores the integration of fuzzy logic with Multi-Criteria Decision-Making (MCDM) methods for applications in sustainable development. This review emphasizes how fuzzy logic effectively addresses uncertainties and vagueness in complex environmental, social, and economic decision processes. It provides robust frameworks essential for comprehensive sustainability assessments, making decision-making more effective in these intricate areas [10].

 

Description

Fuzzy logic systems show significant promise in healthcare, particularly for early diagnosis and prediction where medical data often contains inherent uncertainty. For instance, a novel fuzzy logic system has been developed for the early prediction of cardiovascular disease, leveraging fuzzy inference rules to enhance accuracy and timeliness compared to traditional methods [1]. In a related application, a fuzzy logic-based decision support system assists in the diagnosis and severity assessment of COVID-19. This system integrates medical knowledge and patient symptoms as fuzzy sets, providing nuanced and interpretable evaluations to support clinicians in rapid, informed decision-making [4]. Furthermore, another study proposes a fuzzy logic system integrated with machine learning for predicting diabetes mellitus. The fuzzy logic component effectively handles the vagueness and uncertainty often present in medical datasets, which in turn boosts predictive accuracy and offers transparent reasoning for early diabetes detection [7]. These approaches collectively highlight fuzzy logic's ability to process imprecise information, making it invaluable for critical medical diagnostics.

Extending its utility in healthcare, fuzzy logic also enables specialized diagnostic tools for various conditions. One research introduces a hybrid system combining Fuzzy Cognitive Maps (FCMs), genetic algorithms, and deep neural networks to diagnose depression. Here, the fuzzy component is essential for modeling the complex and often uncertain relationships between symptoms, which significantly enhances the interpretability and accuracy of the diagnostic process [6]. Similarly, a fuzzy logic-based system has been introduced for the early diagnosis of Parkinson's disease, utilizing speech features as input. This system effectively manages the inherent imprecision in vocal biomarkers, providing an early, non-invasive diagnostic tool designed to improve patient outcomes [8]. Moreover, a decision support system employing fuzzy logic has been developed for the early prediction of sepsis in Intensive Care Units. By processing various physiological parameters with fuzzy rules, the system helps identify high-risk patients sooner, enabling timely intervention and potentially improving survival rates for this critical condition [9]. These applications demonstrate the versatility of fuzzy logic in addressing different diagnostic challenges.

Beyond medical applications, fuzzy logic is highly effective in engineering and energy management. An innovative adaptive fuzzy logic energy management system has been designed for hybrid electric vehicles. This system dynamically optimizes power flow between different energy sources, leading to improved fuel efficiency and reduced emissions by adapting to real-time driving conditions and driver behavior [3]. In a similar vein, research proposes an enhanced energy management system for standalone hybrid microgrids. This system integrates solar PhotoVoltaic (PV), wind turbines, and battery storage, employing a fuzzy logic controller to effectively balance power generation and demand. This work improves the stability and efficiency of renewable energy systems in isolated environments [5]. The success of fuzzy logic in these areas highlights its capability to handle dynamic, complex systems with multiple interacting variables, optimizing performance under varying conditions.

Fuzzy logic also extends to adaptive control schemes and broader decision-making frameworks. An adaptive fuzzy control scheme for robotic manipulators addresses challenges such as output constraints and actuator faults. The system uses fuzzy logic to approximate unknown nonlinearities and adapts to handle control input limitations and unexpected failures, thereby enhancing the reliability and performance of robot operations [2]. Lastly, a comprehensive review explores the integration of fuzzy logic with Multi-Criteria Decision-Making (MCDM) methods for applications in sustainable development. This review highlights how fuzzy logic addresses uncertainties and vagueness in complex environmental, social, and economic decision processes, offering robust frameworks for sustainability assessments [10]. These diverse applications showcase fuzzy logic’s strength in areas requiring intelligent control and robust decision-making amidst ambiguity.

Conclusion

Fuzzy logic systems are widely applied across various domains, particularly in managing uncertainty and imprecision inherent in complex data. In healthcare, these systems are instrumental for early disease prediction, including cardiovascular disease, diabetes mellitus, Parkinson's disease, and sepsis, by leveraging fuzzy inference rules and integrating with machine learning techniques. They also aid in the diagnosis and severity assessment of conditions like COVID-19 and depression, offering more nuanced and interpretable evaluations for clinicians. Beyond medical applications, fuzzy logic extends to engineering, where adaptive fuzzy control schemes enhance the reliability and performance of robotic manipulators by approximating unknown nonlinearities and handling actuator faults. It also plays a crucial role in energy management systems. For instance, innovative adaptive fuzzy logic systems optimize power flow in hybrid electric vehicles to improve fuel efficiency and reduce emissions. Similarly, fuzzy logic controllers enhance energy management in standalone hybrid microgrids integrating solar PV, wind turbines, and battery storage, ensuring stability and efficiency of renewable energy systems in isolated environments. Furthermore, fuzzy logic contributes to sustainable development by integrating with Multi-Criteria Decision-Making (MCDM) methods. This helps address vagueness in environmental, social, and economic decision processes, providing robust frameworks for sustainability assessments. The diverse applications underscore fuzzy logic's effectiveness in providing timely diagnostic support, enhancing system reliability, optimizing energy use, and supporting complex decision-making in the face of uncertainty.

Acknowledgement

None

Conflict of Interest

None

References

Ammar QA, Hussein AHA, Abbas IA. "A Novel Fuzzy Logic-Based Approach for Early Prediction of Cardiovascular Disease".Applied Sciences 13 (2023):8206.

Indexed at, Google Scholar, Crossref

Wenjie D, Xiaodong L, Changchun L. "Adaptive Fuzzy Control of Robotic Manipulators with Output Constraints and Actuator Faults".IEEE Trans. Cybern. 52 (2022):12513-12526.

Indexed at, Google Scholar, Crossref

Mohammad RA, Alireza Y, Mehrdad A. "A Novel Adaptive Fuzzy Logic Based Energy Management System for Hybrid Electric Vehicles".IEEE Trans. Transp. Electrif. 7 (2021):2544-2555.

Indexed at, Google Scholar, Crossref

Ghadah SA, Abeer MA, Haya SA. "Fuzzy logic-based decision support system for COVID-19 diagnosis and severity assessment".J. Healthc. Eng. 2021 (2021):6692882.

Indexed at, Google Scholar, Crossref

Omar AA, Khaled HA, Abdul-Rahman MA. "Improved Energy Management System for Standalone Hybrid PV/Wind/Battery Microgrid Using Fuzzy Logic Controller".Energies 15 (2022):7083.

Indexed at, Google Scholar, Crossref

Fatemeh K, Mohammad RK, Mohammad JM. "Diagnosis of depression using Fuzzy Cognitive Maps based on genetic algorithm and deep neural network".J. Biomed. Inform. 129 (2022):104051.

Indexed at, Google Scholar, Crossref

Muhammad I, Muhammad S, Muhammad A. "Fuzzy logic-based approach for predicting diabetes mellitus using machine learning techniques".J. Healthc. Eng. 2021 (2021):9946274.

Indexed at, Google Scholar, Crossref

Muhammad A, Shahid A, Kashif N. "Fuzzy Logic-Based System for Early Diagnosis of Parkinson's Disease Using Speech Features".Comput. Math. Methods Med. 2022 (2022):3672836.

Indexed at, Google Scholar, Crossref

Sarah A, Reem A, Ghadah SA. "A Fuzzy Logic-Based Decision Support System for Early Prediction of Sepsis in Intensive Care Units".J. Healthc. Eng. 2022 (2022):6867702.

Indexed at, Google Scholar, Crossref

Muhammad K, Hassan M, Imran K. "Fuzzy Logic and Multi-Criteria Decision-Making (MCDM) for Sustainable Development: A Review".Sustainability 15 (2023):8089.

Indexed at, Google Scholar, Crossref