Cognitive Computing for Cardiac and Neurological Well-being: AI and Deep Learning Perspectives

Cognitive Computing for Cardiac and Neurological Well-being: AI and Deep Learning Perspectives


  • Muhammad Bin Nazir Department of Computer Science, COMSATS University Islamabad Abbottabad Campus, Email:
  • Shehroz khan Glasgow Caledonian university, Email:
  • Ibrar Hussain Department of Computer Science, University of Punjab, Email:


Cognitive Computing, Cardiac Health, Neurological Disorders, Artificial Intelligence, Deep Learning


Cognitive computing, empowered by artificial intelligence (AI) and deep learning
technologies, holds promise for revolutionizing healthcare, particularly in the domains of cardiac
and neurological well-being. This paper explores the applications of cognitive computing in
predicting, diagnosing, and managing conditions affecting the heart and brain. In the realm of
cardiac health, cognitive computing techniques leverage vast amounts of patient data, including
electronic health records, medical imaging, and wearable sensor data, to develop predictive models
for identifying individuals at risk of cardiovascular events such as heart failure, arrhythmias, and
myocardial infarction. These models enable early detection, risk stratification, and personalized
intervention strategies, ultimately improving patient outcomes and reducing healthcare costs.
Similarly, in the field of neurological health, cognitive computing algorithms analyze complex
patterns in brain imaging data, genetic profiles, and clinical records to assist in the diagnosis and
management of neurological disorders such as Alzheimer's disease, Parkinson's disease, and
stroke. By uncovering subtle biomarkers and disease signatures, cognitive computing approaches
facilitate early detection, disease monitoring, and targeted treatment planning, leading to better
prognoses and enhanced quality of life for patients. Moreover, cognitive computing technologies
enable the integration of disparate data sources and the development of comprehensive decision
support systems for healthcare providers. By synthesizing clinical knowledge, scientific evidence,
and real-time patient data, these systems empower clinicians with actionable insights, facilitating
more informed decision-making and personalized care delivery. However, the widespread
adoption of cognitive computing in healthcare is not without challenges. Ethical considerations
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regarding patient privacy, algorithmic bias, and regulatory compliance must be carefully addressed
to ensure the responsible and equitable use of these technologies. In conclusion, cognitive
computing represents a paradigm shift in healthcare, offering unprecedented opportunities for
improving cardiac and neurological well-being through AI and deep learning perspectives. By
harnessing the power of data-driven insights and intelligent algorithms, cognitive computing has
the potential to transform healthcare delivery, enhance clinical decision-making, and ultimately,
advance the field of precision medicine.




How to Cite

Muhammad Bin Nazir, Shehroz khan, & Ibrar Hussain. (2024). Cognitive Computing for Cardiac and Neurological Well-being: AI and Deep Learning Perspectives . Revista Espanola De Documentacion Cientifica, 18(02), 180–208. Retrieved from