Medical diagnosis using machine learning

Studying physiological data, environmental influences, and genetic factors allow practitioners to diagnose diseases early and more 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:

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

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, ranging from details of 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:

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.

Conclusions

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

As we want to help in this mission to improve healthcare and push researchers to get the best results they can, we are offering special prices for academic programs.

Related solutions:

Related examples:

References:

Subscribe To Our Newsletter