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.


  1. Objectives.
  2. Benefits.
  3. Approach.
  4. Conclusions.


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.


Some benefits of leveraging machine learning in medicine are the following:


Discover which variables are more associated with the risk of suffering a disease.


Diagnose diseases earlier and more accurately.


Schedule hospital visits only when patient care is needed.


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, it is possible to apply these solutions in healthcare without that knowledge with tools like Neural Designer.

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, designs the predictive model, and tests it against new cases.


The study of relevant factors and different types of data jointly allows practitioners to diagnose diseases more effectively.


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