Predicting Clinical Risk Factors of Diabetes UsingMultiple Machine Learning Techniques to Resolve Class Imbalance
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1 Paper Introduction
Title: Prediction of Clinical Risk Factors of Diabetes Using Multiple Machine Learning Techniques Resolving Class Imbalance
Diabetes is the most common and fastest-growing disease, affecting a large population from all age groups each year and shortening lifespans. The high incidence rate increases the significance of early diagnosis. Diabetes can also lead to other complex complications, such as cardiovascular diseases, kidney failure, stroke, and damage to vital organs. Early diagnosis of diabetes can reduce the likelihood of it progressing to chronic and severe states. Identifying and analyzing risk factors associated with different spinal attributes helps determine the prevalence of diabetes in medical diagnostics.
Measuring and identifying the prevalence of early diabetes reduces the likelihood of future complications. In this study, data from the NHANES collective dataset from 1999-2000 to 2015-2016 was used. The purpose of this research is to analyze and identify potential risk factors associated with diabetes using logistic regression and variance analysis, and to identify anomalies using various supervised machine learning algorithms. Class imbalance and anomaly issues were addressed, and the experimental results indicated that age, blood-related diabetes, cholesterol, and BMI are the most important risk factors associated with diabetes. Meanwhile, the random forest classification method achieved the highest accuracy score of .90.
Source code screenshot
2 Paper Figures
Figure 1: Identifying and Handling Outliers
Figure 2: Graphical Representation of the Models Used
Figure 3: Feature Importance Based on Variance Analysis F-Score
Figure 4: Structure of Artificial Neural Network (ANN)
Table 1: P-values, Odds Ratios, and Confidence Intervals Using Logistic Regression for Individual Risk Factors
Table 2: Comparison of Accuracy and AUC Scores Under Different CV Values
Conclusion
(1) Predicting Clinical Risk Factors of Diabetes Using Multiple Machine Learning Techniques to Resolve Class Imbalance, follow the official account Novice Medical Research, reply with 2501c1 to get the material link. Materials have been uploaded to the mini program:
(2) Custom Data Analysis, Core and SCI Journal Recommendations, specific inquiries can be made by scanning the code to consult course assistants.
(3) Novice Medical Research Learning Center is officially launched! Many courses are free! Course members can easily learn all or specialized courses without worries; Universal Membership can also get free access to course materials, premium source code, offline databases, medical data, and other quality resources. Thesis Guidance Students can receive a one-year Universal Membership for free.
(4) Medical Public Data Database Learning Camp has opened, elite students in thesis guidance can receive recommended reviewer information, welcome to consult course assistants!
Learn More | Medical Public Database Learning Camp
(5) Data Extraction and Custom Data Analysis, specific inquiries can be made by scanning the code to consult course assistants.
(6) Video Course Recommendations