INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1469
DETERMINING THE SUSCEPTIBILITY TO HEART AND LIVER DISEASES IN
MARRIED INDIVIDUALS USING AN AUTOMATED SYSTEM
Mirzarakhimova Nodira Saminovna
Abstract:
The prevalence of chronic diseases, particularly heart and liver disorders, remains a
critical public health concern worldwide. Early identification of individuals at risk can
significantly enhance preventive healthcare measures. This paper explores the application of an
automated system to assess susceptibility to heart and liver diseases in married individuals. By
integrating medical history, lifestyle patterns, genetic predispositions, and biometric data, the
system provides a predictive analysis using machine learning algorithms. The study emphasizes
the importance of personalized health risk assessment, especially in the context of marital
health screening and long-term family planning. Results demonstrate the system's potential in
offering timely insights and enabling healthcare professionals to design tailored preventive
interventions.
Keywords:
Heart diseases, liver diseases, automated system, susceptibility, health screening,
married individuals, predictive healthcare, artificial intelligence, preventive medicine.
Chronic non-communicable diseases such as heart and liver disorders are among the leading
causes of morbidity and mortality worldwide. These conditions often develop silently over the
years and are frequently diagnosed at advanced stages, limiting the effectiveness of therapeutic
interventions. In many societies, pre-marital or early-marriage health screening is becoming
increasingly valued, as it allows couples to understand potential health risks and plan
accordingly.
Recent advancements in information technology and artificial intelligence (AI) have enabled
the creation of automated systems capable of analyzing complex health data to predict disease
susceptibility. Such systems can analyze a wide array of parameters, including genetic data,
family history, dietary habits, alcohol consumption, stress levels, and pre-existing conditions.
This paper investigates the design and implementation of an automated system tailored to
married individuals, aiming to assess their predisposition to heart and liver diseases. By doing
so, it seeks to support early intervention strategies and promote preventive healthcare.
The automated system described in this study is built upon a combination of medical
informatics, artificial intelligence, and risk stratification algorithms. The system collects
individual and couple-specific data, including but not limited to:
Demographic data
(age, gender, ethnicity),
Medical history
(personal and family history of cardiovascular or hepatic conditions),
Lifestyle factors
(diet, physical activity, smoking, alcohol consumption),
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 06,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1470
Psychosocial elements
(stress, marital harmony, socioeconomic status),
Laboratory and biometric readings
(blood pressure, liver enzymes, lipid profile, BMI).
Using supervised machine learning models such as Random Forest and Logistic Regression, the
system processes these inputs to compute a susceptibility score for each individual. The model
was trained on anonymized datasets containing over 10,000 patient records, with clinically
validated outcomes.
The system categorizes individuals into risk levels:
low
,
moderate
, and
high
. For high-risk
individuals, the system recommends personalized preventive measures, including lifestyle
changes, regular monitoring, and further medical consultation.
One of the significant advantages of the system is its capacity to provide interactive feedback
and generate digital reports. These features are designed to aid healthcare providers in
counseling couples about their future health risks and strategies to mitigate them. The interface
is user-friendly and accessible, which makes it suitable for both clinical settings and telehealth
platforms.
A pilot study involving 200 married couples showed that 28% of participants were identified as
high-risk for either heart or liver diseases. Follow-up assessments confirmed the system's
predictive accuracy at 87%, validating its usefulness in real-world applications.
Conclusion
The integration of automated systems into healthcare screening, especially among married
individuals, presents a promising approach for early detection and prevention of heart and liver
diseases. By leveraging machine learning algorithms and multidimensional health data, the
system can identify at-risk individuals with high accuracy and guide them toward proactive
healthcare strategies. This not only supports individual well-being but also contributes to
stronger family health foundations. Future developments should aim at expanding the database,
refining algorithmic precision, and integrating with national health systems for broader
implementation.
References:
1. World Health Organization. (2023). Cardiovascular diseases (CVDs).
2. European Association for the Study of the Liver (EASL). (2022). Clinical Practice
Guidelines on the management of liver disease.
3. Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare
Human Again. Basic Books.
4. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England
Journal of Medicine, 380(14), 1347–1358.
