Introduction
Studying physiological data, environmental influences, and genetic factors allows practitioners to diagnose diseases early and effectively.
Machine learning allows us to build models associating various variables with a disease.
The data science and machine learning platform Neural Designer combines different data types into a single model to better diagnose diseases.
Contents
1. Objectives
The analysis of clinical data enables us to understand the biological mechanisms that underlie diseases and how risk factors influence their development.
Thankfully, a large amount of data is currently available to clinicians. These data range from clinical symptoms to various biochemical assays and outputs of imaging devices.
These are some examples of types of data that could be useful to make an accurate medical diagnosis using machine learning:
- Disease data: Physiological measurements and information about known diseases or symptoms that an individual has experienced.
- Environmental data: Information about an individual’s environmental exposures, such as smoking, sunbathing, weather conditions, etc.
- Genetic data: All or critical parts of the DNA sequence of an individual.
2. Benefits
2.1. Find risk factors
Discover which variables are most closely associated with a patient’s risk of developing a disease and how they may influence its progression.
2.2. Increase diagnosis efficiency
Machine learning detects diseases earlier, allowing timely intervention and improving patient care.
2.3. Reduce unnecesary hospital visits
Machine learning helps determine when a patient truly needs medical attention.
3. Approach
Machine learning can improve disease diagnosis and patient care, but it often requires programming skills and specialized equipment.
Tools like Neural Designer simplify this process, enabling healthcare professionals to analyze clinical data, identify key variable relationships, build predictive models, and test them on new cases without coding.
4. Conclusions
- The study of relevant factors and different types of data jointly allows practitioners to diagnose diseases more effectively.
