糖尿病
适应(眼睛)
计算机科学
人工智能
医学
机器学习
生物
内分泌学
神经科学
作者
Yifei Su,Chengwei Huang,Weidong Yin,Xin Lyu,Li Ma,Zhenhuan Tao
标识
DOI:10.1016/j.bspc.2022.104381
摘要
Conventional disease prediction models frequently have imbalanced or insufficient observed samples. In a real-world situation, the learned model from an imbalanced dataset may have poor generalization ability. In this paper, we use machine learning methods to investigate age adaptation in Diabetes Mellitus (DM) risk prediction. Typical machine learning algorithms for DM prediction include Linear Regression (LR), Logistic Regression, Polynomial Regression (PR), Neural Network (NN), Support Vector Machines (SVM), Random Forest (RF), and XGboost (XGB). Based on feature compensation and soft decision threshold adjustment, we propose a novel age adaptation algorithm. The results were validated using the publicly available Pima Indian Diabetes Dataset. The experimental results show that the disease risk prediction model’s efficiency and accuracy are promising. The performance dropped for different age groups when using the original features. Using the compensated features, the accuracy rates for DM models were improved considerably. Age adaptation methods can be trained on one age group and adapted to another, solving the problem of data scarcity in a specific age range. • Diabetes Mellitus risk prediction is studied with various machine learning models. • Feature distribution among different age groups are anlyzed. • A novel feature compensation network is proposed. • Generalization ability on unseen age groups is achieved.
科研通智能强力驱动
Strongly Powered by AbleSci AI