Interpreting black-box models: a review on explainable artificial intelligence

V Hassija, V Chamola, A Mahapatra, A Singal… - Cognitive …, 2024 - Springer
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

The role of AI in hospitals and clinics: transforming healthcare in the 21st century

S Maleki Varnosfaderani, M Forouzanfar - Bioengineering, 2024 - mdpi.com
As healthcare systems around the world face challenges such as escalating costs, limited
access, and growing demand for personalized care, artificial intelligence (AI) is emerging as …

AI-powered innovation in digital transformation: Key pillars and industry impact

A Aldoseri, KN Al-Khalifa, AM Hamouda - Sustainability, 2024 - mdpi.com
Digital transformation systems generate a substantial volume of data, creating opportunities
for potential innovation, particularly those driven by artificial intelligence. This study focuses …

Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances

P Khan, MF Kader, SMR Islam, AB Rahman… - Ieee …, 2021 - ieeexplore.ieee.org
Brain is the controlling center of our body. With the advent of time, newer and newer brain
diseases are being discovered. Thus, because of the variability of brain diseases, existing …

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

A Raza, HUR Siddiqui, K Munir, M Almutairi, F Rustam… - Plos one, 2022 - journals.plos.org
Maternal health is an important aspect of women's health during pregnancy, childbirth, and
the postpartum period. Specifically, during pregnancy, different health factors like age, blood …

An improved VGG model for skin cancer detection

H Tabrizchi, S Parvizpour, J Razmara - Neural Processing Letters, 2023 - Springer
Skin cancer is one of the most prevalent malignancies in humans and is generally
diagnosed through visual means. Since it is essential to detect this type of cancer in its early …

[HTML][HTML] Machine learning in the prediction of cancer therapy

R Rafique, SMR Islam, JU Kazi - Computational and Structural …, 2021 - Elsevier
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many
cancer-related deaths. Resistance can occur at any time during the treatment, even at the …

Comparative study of optimum medical diagnosis of human heart disease using machine learning technique with and without sequential feature selection

GN Ahmad, S Ullah, A Algethami, H Fatima… - ieee …, 2022 - ieeexplore.ieee.org
Predicting heart disease is regarded as one of the most difficult challenges in the health-
care profession. To predict cardiac disease, researchers employed a variety of algorithms …

[HTML][HTML] A novel proposal for deep learning-based diabetes prediction: converting clinical data to image data

MF Aslan, K Sabanci - Diagnostics, 2023 - mdpi.com
Diabetes, one of the most common diseases worldwide, has become an increasingly global
threat to humans in recent years. However, early detection of diabetes greatly inhibits the …

An improved ensemble-based cardiovascular disease detection system with chi-square feature selection

AE Korial, II Gorial, AJ Humaidi - Computers, 2024 - mdpi.com
Cardiovascular disease (CVD) is a leading cause of death globally; therefore, early
detection of CVD is crucial. Many intelligent technologies, including deep learning and …