Six supervised learning models were developed and compared for diabetes prediction using a dataset and compared for diabetes prediction using a 100k patients records with eight clinical features including gender, age, hypertension, smoking history, heart disease, BMI, HbA1c level, and blood glucose level.
It is indicated that a rigorously conducted methodology and interpretability in machine learning development are crucial in creating machine learning solutions in healthcare decision support, which is the pathway to real applications in diabetes risk assessment.
T. Khan, M. Saeed, Majid Hussain et al.· Scientific Reports· 0 citations
In the future, it is essential to establish a standardized validation framework, develop interpretable algorithms, integrate wearable non-invasive markers, and implement Bayesian racial modeling to promote early screening and personalized intervention, thereby revolutionizing the clinical prevention paradigm.
Dingnan Wu· Proceedings of the 3rd Inter...· 0 citations
Diabetes has become a health problem worldwide. It often goes unnoticed until it causes health issues. Finding diabetes early using a lot of health and personal data can help reduce the diseases impact and healthcare costs. This study proposes a machine learning system for diabetes prediction. This system uses techniqu...
Thatikonda Krishna Kalyan Gupta, Oruganti Yashwanth Reddy, I. S et al.· 2026 International Conferenc...· 0 citations
Cardiovascular disease, as a highly prevalent chronic condition, has shown a continuously rising incidence in China and now ranks as the leading cause of death among both urban and rural residents. Mainstream Cardiovascular disease risk prediction models have mostly been developed based on European and American populat...
Longfa Chu, Jing Lin, Zekai Li et al.· 2026 IEEE 27th China Confere...· 0 citations
A machine learning-based framework enhanced with explainability is introduced, built around a structured data preparation process that handles categorical encoding, numerical scaling, and minority class oversampling through the SMOTE technique, positioning it as a trustworthy tool for assisting medical professionals in...
N. J, Deekshitha U, K. V· International Journal of Sci...· 0 citations
The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare and underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations
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