Medical diagnosis using machine learning
Studying physiological data, environmental influences, and genetic factors allow practitioners to diagnose diseases early and effectively.
Machine learning allows us to build models that associate a broad range of variables with a disease.
The data science and machine learning platform Neural Designer
brings together different data types into a single model to better diagnose diseases.
Contents:
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 types of 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 key parts of the DNA sequence of an individual.
Benefits
Some benefits of leveraging machine learning in medicine are the following:
FIND RISK FACTORS
Discover which variables are more associated with the risk of suffering a disease.
INCREASE DIAGNOSIS EFFICIENCY
Diagnose diseases earlier and more accurately.
REDUCE UNNECESSARY HOSPITAL VISITS
Schedule hospital visits only when patient care is needed.
Approach
Machine learning has proven to be an effective way of diagnosing diseases.
The problem with this technique is that it can be difficult to apply if you do not have programming skills or dedicated equipment.
Nevertheless, with tools like Neural Designer, it is possible to apply these solutions in healthcare without that knowledge.
Neural Designer is a machine learning software with a friendly user interface that implements neural networks and allows you to build machine learning models quickly and efficiently.
It analyzes clinical data, finds relations between the diagnostic variables, design the predictive model, and tests it against new cases.
Conclusions
The study of relevant factors and different types of data jointly allows practitioners to diagnose diseases more effectively.
