Big data is becoming more and more relevant as more advanced ways of putting it to use and harvesting its positives are being introduced into the market. Due to this rise, healthcare practitioners continue to seek ways to learn new ways to read data and put it to use in more efficient ways to advance their health practice and provide services in more efficient and cost-effective ways. The Healthcare Analytics Conference 2018 is one of the most comprehensive, educational, and leading-edge forum for data and analytics-driven best practices that improve outcomes. One of the main focuses of the conference will be on predictive analysis and big data.
Speaking of advances in the healthcare industry, Artificial Intelligence has definitely continued to grow in popularity and usefulness in the healthcare industry and so has machine learning. As many seek to take advantage of these advances and apply it in new and cutting-edge ways, let’s’ talk about the machine learning languages and algorithms currently out there
Machine Learning Algorithms
The existing ones that are the most popular are
- Naïve Bayes Classifier Algorithm
- K Means Clustering Algorithm
- Support Vector Machine Algorithm
- Apriori Algorithm
- Linear Regression
- Logistic Regression
- Artificial Neural Networks
- Random Forests
- Decision Trees
- Nearest Neighbours
- Machine Learning Algorithms
The first two languages, their advantages and how they operate is explained below:
Naïve Bayes Classifier Algorithm
It would be difficult and practically impossible to classify a web page, a document, an email or any other lengthy text notes manually. This is where Naïve Bayes Classifier machine learning algorithm comes to the rescue. A classifier is a function that allocates a population’s element value from one of the available categories. For instance, Spam Filtering is a popular application of Naïve Bayes algorithm. Spam filter here is a classifier that assigns a label “Spam” or “Not Spam” to all the emails. Naïve Bayes Classifier is amongst the most popular learning method grouped by similarities, that works on the popular Bayes Theorem of Probability- to build machine learning models particularly for disease prediction and document classification. It is a simple classification of words based on Bayes Probability Theorem for subjective analysis of content.
When to use the Machine Learning algorithm – Naïve Bayes Classifier?
- If you have a moderate or large training data set.
- If the instances have several attributes.
- Given the classification parameter, attributes which describe the instances should be conditionally independent.
- Applications of Naïve Bayes Classifier
- Naive Bayes Algorithm Applications
Sentiment Analysis – It is used by Facebook to analyze status updates expressing positive or negative emotions.
Document Categorization – Google uses document classification to index documents and finds relevancy scores i.e. the PageRank. PageRank mechanism considers the pages marked as important in the databases that were parsed and classified using a document classification technique.
Naïve Bayes Algorithm is also used for classifying news articles about Technology, Entertainment, Sports, Politics, etc.
Email Spam Filtering-Google Mail uses Naïve Bayes algorithm to classify your emails as Spam or Not Spam
Advantages of the Naïve Bayes Classifier Machine Learning Algorithm
- Naïve Bayes Classifier algorithm performs well when the input variables are categorical.
- A Naïve Bayes classifier converges faster, requiring relatively little training data than other discriminative models like logistic regression when the Naïve Bayes conditional independence assumption holds.
- With Naïve Bayes Classifier algorithm, it is easier to predict the class of the test data set. A good bet for multi-class predictions as well.
- Though it requires conditional independence assumption, Naïve Bayes Classifier has presented good performance in various application domains.
- Data Science Libraries in Python to implement Naïve Bayes – Sci-Kit Learn
K Means Clustering Algorithm
K-means is a popularly used unsupervised machine learning algorithm for cluster analysis. K-Means is a non-deterministic and iterative method. The algorithm operates on a given data set through a pre-defined number of clusters, k. The output of K Means algorithm is k clusters with input data partitioned among the clusters.
For instance, let’s consider K-Means Clustering for Wikipedia Search results. K Means clustering algorithm can be applied to group the web pages that talk about similar concepts. So, the algorithm will group all web pages that talk about Orange as a fruit into one cluster, Orange as a color into another cluster and so on.
Advantages of using K-Means Clustering Machine Learning Algorithm
- In case of globular clusters, K-Means produces tighter clusters than hierarchical clustering.
- Given a smaller value of K, K-Means clustering computes faster than hierarchical clustering for a large number of variables.