Top Machine Learning Algorithms Used In The Healthcare Industry?

machine learning algorithm
Written by Bilal Munsif

Today, I thought of sharing new knowledge on top machine learning and algorithms. Specifically used in the field of healthcare. Everybody is aware of Linear regression and logistic regression. You are aware of those regressions then you should go through the article having in-depth knowledge of Linear regression. Logistic regression before starting with the support vector machine. 

Also ReadHow Much Does It Cost To Develop A Healthcare Mobile App?

Support Vector Machine

SVM Support vector machine deals with finding a hyperplane, which is an N-dimensional space(N-number features) that specifies the data point. 

Support vector machines help in protein classification, image processing and segregation, and text categorization. It is the most standardize one, and it is already use for giving medical adherence to the heart patient. And helping millions of patients to cure. It is the best learning model for the regression and detection of outlines. 

Artificial Neural Networks

It is the only algorithm, inspired by animal brains. It actually receives a signal from one layer. Sends it to another, and analyzes itself by the situation, what to perform. 

We have to discuss the neural network. What does a neural network do? 

It reflects the behavior of the human brain and tries to solve the problem and common issues, by programming with the computer.  It is distribute into CNN (convolutional neural network) and RNN(Recurrent neural network).

Imaging is the most important process in healthcare, it can help us in predicting diseases before the symptoms arise.  There are many processes such as colonoscopy, mammograms, etc. in this whole CNN signifies its crucial role and RNN signifies its pattern recognition in medical data analysis. 

Logistic regression 

Logistic regression is use to predict the scenario of healthcare, it helps in assisting the discrete values (usually from 0 to 1) from a set of different variables. It helps in calculate the profitability of an event by measuring to a logit function, it is known logit regression. 

There are various types of methods that can be use to eliminate some features from it. Such as: 

  • Include interaction terms
  • Eliminate features 
  • Regularize techniques

Naive Bayes algorithm

Basically, it works with the intersection of the featured class, it assumes and clarifies that the provided feature class is unrelated to the second feature class. It checks the profitability of the particular outcome of the feature class. 

KNN (K- nearest neighbours) Algorithm

KNN algorithms have the specialty that they apply to both types of regression problems, in the medical field it is known it stores all the available information from the database and simplifies the new cases take votes of several cases.

In a simple way, we look at it like this, you are give a task to gather information about an individual, then you start with his family and friends. 

There are some points that you should keep in mind before planning for KNN:

  • It will be more expensive, 
  • It did not dilute the data properly,
  • Data still need to be preprocesse, 

Random forest algorithm 

There is a collective decision of the trees in this algorithm, the collective decision by the trees is known random forest. Any query submit to the trees solve with a joint decision or we can say a vote of the trees. 

Each tree is plant and grown in some specific ways as follows: 

  1. A random sampling of the trees done by collecting the N number of samples with the training sets. 
  2. zdcA random sampling of the trees done by collecting the N number of samples with the training sets. 
  3. A random sampling of the trees done by collecting the N number of samples with the training sets. 

Dimensionality reduction element/algorithm: 

Analysis and study of several machine learning algorithms are need for better understanding.

Every sector in this world deals with data and information. eing a specialist it is necessary to judge the raw data and its uses so that it will benefit all the sectors like corporates, hospitals, and government offices. 

Processes like factor analysis, decision tree, missing value ratio and decision analysis all these methods can help you find relevant information. 

Gradient boosting algorithm: 

These gradient-boosting algorithms deal with larger data sets to make decisions with more accuracy. It collects all the from various sources and is able to make a crisp decision it assembles the predictive power and makes the decision. 

Currently, Kaggle, crowd analytics, etc. are use as the best competitors, these are the most achievable and highly used algorithms today, but to be precise in your task you can use them with R codes, and Pythons to have accuracy in your outcome. 

Possibilities in the near future: 

The possible outcome is that in the near future, experts will make some new distinct models with newly featured machine learning algorithms, where the future seems to be full of artificial intelligence (in nearly 2040). You can approach the studies in these fields if you are serious about machine learning, and AI learning


After discussing all these, I am sure you will have some ideas for the future and some techniques to build a career. Innovations are done in the field of medical science too rapidly. Another tip for beginners, you think of it just once, then every small thing not be done without the help of technology in the healthcare sector after the pandemic, the top healthcare app development company; is growing at a faster pace. 

About the author

Bilal Munsif

Hi, I'm Bilal, a technology expert with a Bachelor of Science in Computer Science. I am passionate about exploring the latest trends and advancements in the tech industry and have dedicated my career to staying up to date with the latest developments. Shopping Tips & Reviews & Find Hot Deals - Get FREE online coupons and promo codes for the stores you love!