Work

Feature importance and model performance for prediabetes prediction: A comparative study

Year: 2024

Type: article

Abstract: Machine learning models enhance prediabetes prediction using diverse data. Key predictors include cholesterol, BMI, hypertension, age, and income. Random Forest demonstrated the highest accuracy and recall in predictions. Insights support cost-effective, large-scale prediabetes screening solutions. Findings help identify high-risk groups for early intervention and prevention.

Source: Journal of King Saud University - Science

DOI: 10.1016/j.jksus.2024.103583

Authors: Saeed Awad M Alqahtani, Hussah M Alobaid, Jamilah Alshammari, Safa A Alqarzae, Sheka Yagub Aloyouni, Ahood A. Al-Eidan, Salwa Alhamad, Abeer Almiman, Fadwa M Alkhulaifi, Suliman Alomar

Institutions: Taibah University, King Saud University, Princess Nourah bint Abdulrahman University, Imam Abdulrahman Bin Faisal University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $1,600

Funders: King Saud University, Princess Nourah Bint Abdulrahman University

Awards: G6833966537, G7859999938

OpenAlex ID: W4404902408

FWCI: 1.204

Cites: 34

Cited by: 4

Related to: 10