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Research Article | DOI: https://doi.org/10.31579/2690-1919/439
Department of Haematology, Oncology and Immunology, Philipps University Marburg, Marburg, Germany
*Corresponding Author: Gerhard Zugmaier, MD, Department of Haematology, Oncology and Immunology, Philipps University Marburg, Marburg, Germany.
Citation: Gerhard Zugmaier, (2025), Boolean Algebra (Mathematical Logic) for computational Differential Diagnosis in Medicine, J Clinical Research and Reports, 18(1); DOI:10.31579/2690-1919/439
Copyright: © 2025, Gerhard Zugmaier. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 18 November 2024 | Accepted: 06 January 2025 | Published: 15 January 2025
Keywords: Boolean Algebra; differential diagnosis; clinical symptoms and signs; medicine
Background: Medical diagnosis has become challenging due to the complexity of disease definition. An arsenal of methods is necessary to conduct a correct differential diagnosis
Aim: This study used computational medicine in form of Boolean algebra to assess diagnosis. We used Boolean algebra as rigid framework designed to aid in differential diagnosis.
Results: Each symptom or test result was represented as a Boolean variable. Each condition or disease was represented as a Boolean variable.
Conclusion: Boolean algebra can be used to model and simplify decision-making processes in differential diagnosis.
Precise diagnosis is essential for successful treatment. It enables exact definition of disease and patient-adapted treatment. Precise and swift diagnosis is an essential-condition for successful handling of patients with any disease The international statistical classification of diseases and related health problems (ICD) from the World Health Organization (WHO) has established a defined process of disease definition [3] Although numerous advanced technologies for diagnosis have become available [15,16,1,3], clinical a symptoms and signs are still the essentials of any differential diagnostic process. Boolean algebra provides a rigorous framework for designing and optimizing diagnostic systems, ensuring consistency and efficiency. As shown previously [20-24]. In this study we set out to use Boolean algebra as part of computational medicine for differential diagnosis based on clinical symptoms and signs as shown previously for definition of disease and for abnormal laboratory values [20-24].
Symbols of Boolean Algebra
1.Variables:
o Each Boolean variable can take one of the values 1 or 0.
2.Operations:
o AND:
o Inclusive OR:
o NOT:
o XOR:
Example: 1 ⊕ 0 = 1, 1 ⊕ 1 = 0
o XNOR:
"if A, then B” is: expressed as A → B = ¬ A v B. The arithmetic rules of the inclusive OR are applied.
In the formulas the following operators take precedence:
v, ⊕ over =17
Examples of multiple Representations for the Same Operations7:
AND:
A⋅B or AB: Common in traditional Boolean algebra and engineering
A ∧ B: Used in formal logic and computer science
A AND B Seen in programming pseudocode or textual descriptions
OR (Inclusive OR):
A + B: Traditional Boolean algebra
A ∨ B: Used in logic and theoretical fields
A OR B: Common in programming and textual representations
XOR (Exclusive OR):
A ⊕ B: Theoretical computer science and mathematics
A XOR B: Textual or programming contexts
In this section clinical symptoms and signs are listed with the combinations of clinical symptoms and signs leading to the correct diagnosis. The symbols are explained in Materials and Methods. The major clinical signs or clinical symptoms are listed in alphabetical order.
Bell’s palsy → herpes simplex 1
Blood pressure ↓ ∧ normal pulse → autonomic insufficiency
Delirium → metabolic encephalopathy
Epitrochlear lymph nodes palpable → infections forearm v lymphoma v sarcoidosis v tularemia v syphilis
Fever > 38 C ∧ tachycardia ∧ hypertension ∧ delirium ∧ rigidity ∧ ¬ clonus → neuroleptic malignant syndrome
Fever > 38 C ∧ tachycardia ∧ hypertension ∧ delirium ∧ rigidity ∧ clonus → serotonin syndrome
Further from equator ∧ opticus neuritis → multiple sclerosis
Herpes zoster ad nose → corneal herpes zoster
Indolent lymph nodes ∧ advanced age ↑ ∧ smoker → head and neck cancer
Lemiere’s syndrome (septic thrombophlebitis of vena jugularis interna) → septic pulmonary emboli
Lymph nodes ↑ ∧ spleen ↑ → lymphoma v lymphatic leukemia v mononucleosis
Nephrotic syndrome → risk ↑ of venous thrombosis
Osler lesions → immune complex nephritis
Painless jaundice → cancer pancreas head
Paroxysmal nocturnal hemoglobinuria → iron ↓
Postprandial blood pressure ↓ ∧ reversal of circadian pattern → orthostatic hypotension
Pulsus paradoxus → cardiac tamponade v pericarditis consrictiva v chronic obstructive pulmonary disease v asthma
Recurrent aphthous ulcers → Behcet’s v Crohn’s disease
Sudden thoracal pain → pneumothorax
Unilateral right varicocele → obstruction of vena cava inferior
Widened pulse pressure → persistent ductus arteriosus
We have applied Boolean Algebra to differential diagnosis of clinical symptoms and signs. The clinical symptoms and signs were used in this study as described in Suneja et al. [4] Boolean algebra is part of computational medicine to calculate complex permutations. It includes a number system with the sole integers 0 and 1. Boolean arithmetic has been defined by Shannon [13]. In Boolean arithmetic 1 + 1 = 1 is correct. Other than the rules of addition apply.
In this study, we applied Boolean operations to standardize differential diagnosis based on clinical symptoms and signs.
Mathematics is indispensable in medicine, serving as a bridge between theoretical concepts and practical applications. It enables precise modeling, efficient computation, and improved patient outcomes, transforming the landscape of healthcare and biomedical research [9].
Boolean algebra has significant applications in computational medicine, where binary systems play a crucial role in modeling, analyzing, and solving medical problems. It is particularly effective in areas requiring clear decision-making, rule-based systems, and systematic modeling of biological or medical data. IT is foundational in developing decision-support systems that assist healthcare professionals in diagnosing and treating diseases [2, 8, 18, 5, 11, 10, 20-24]
The term “mathematical logic” is used in a strict mathematical context. It should not be confused with logic as basis of any scientific inquiry [6,14].
The lack of standardization in mathematical symbols can cause confusion, miscommunication, and inefficiencies in education, research, and communication. This issue stems from historical, cultural, and contextual differences in how symbols are used and interpreted across disciplines, regions, and even individuals [19]).
Boolean algebra's confusing notation arises from the diversity of its applications across mathematics, computer science, engineering, and mathematical proof theory. Each field often adopts conventions that suit its specific needs, leading to multiple ways to express the same operations7.
This study has various limitations. Boolean algebra, while useful for modeling logical systems and binary decision-making processes, has significant limitations when applied to differential diagnosis in complex medical or problem-solving contexts. These limitations arise due to the inherent oversimplification in mathematical logic, which may not adequately reflect the nuanced realities of medical decision-making. Boolean algebra cannot incorporate probabilities or degrees of uncertainty, which are crucial in medical diagnosis. Differential diagnosis often relies on Bayesian reasoning or probabilistic methods or both, which Boolean algebra does not support. Machine learning-based methods, which can learn from historical patient data, are more effective in modern diagnostic tools. Bayesian reasoning, probabilistic methods and Boolean algebra are not mutually exclusive but complement each other, since all 3 methods have advantages and limitations. The pros and cons of the 3 methods are detailed in Table 1.
| Feature | Boolean Algebra | Probabilistic Models | Machine Learning Models |
|---|---|---|---|
| Binary or Continuous Data | Binary only | Handles probabilities, continuous data | Handles both and abstract patterns |
| Handles Uncertainty | No | Yes | Yes |
| Learns from Data | No | Limited | Yes |
| Temporal Reasoning | No | Limited | Yes (e.g., recurrent neural networks) |
| Incorporates Interdependencies | Limited | Yes | Yes |
Table 1: Comparison to Probabilistic and Machine Learning Models
Boolean algebra has a role in structured, rule-based diagnostic systems but falls short in addressing the complexities of real-world differential diagnosis. Probabilistic reasoning, machine learning, and temporal modeling offer more robust solutions for modern medical diagnostic challenges. Boolean models are best used in conjunction with these advanced tools rather than as standalone systems.
Journal of Women Health Care and Issues By the present mail, I want to say thank to you and tour colleagues for facilitating my published article. Specially thank you for the peer review process, support from the editorial office. I appreciate positively the quality of your journal.
