Potential value and impact of data mining and machine learning in clinical diagnostics

Area of Science:

• Biomedical Informatics
• Computational Biology
• Data Science

Background:

• Data mining, utilizing AI and machine learning, uncovers relationships in large datasets.
• Previous research demonstrates data mining's efficacy in predicting type 2 diabetes mellitus.
• Machine learning has been applied to assess biomarkers and cardiovascular disease risk.

Purpose of the Study:

• To review studies analyzing biochemical biomarker data with machine learning methods.
• To summarize the potential applications of data mining in clinical diagnosis.
• To highlight the growing use of data mining in supporting clinical diagnostics.

Main Methods:

• Review of existing literature on data mining and machine learning applications in disease risk assessment.
• Analysis of studies employing machine learning for biomarker analysis and disease prediction.
• Identification of specific machine learning techniques used in various medical fields.

Main Results:

• Machine learning effectively predicts type 2 diabetes, cardiovascular disease risk, and renal disease outcomes.
• Techniques like random forests excel in predicting chronic kidney disease.
• Machine learning aids in diagnosing Alzheimer's disease and identifying mental illness risk factors.

Conclusions:

• Data mining and machine learning offer significant potential for advancing clinical diagnostics.
• These computational methods can improve disease risk assessment and patient outcomes.
• Further research is warranted, particularly in areas like cancer and bacterial diseases.