UNAIR NEWS –Recently, the termmachine learningin the medical world has been used often in public.As a part of statistics,, and computer science,machine learning isalso known aspredictive analyticsorstatistical learning.
The role ofmachine learninginvery diverse, such as identification, diagnosis, prediction of a disease,smart health records, medical imaging, and so on.So it’s no wonder that many people considermachine learning to bevery helpful.
As expressed by, as a lecturerFaculty of Advanced Technology and Multidiscipline (FTMM), ifmachine learningalgorithmscan be applied optimally, then a doctor can diagnose a disease earlier and more accurately.
“Some doctors have started to look atmachine learningalgorithmsto be applied to data processing.This will certainly provide high efficiency and accuracy, 漵aid the lecturer who graduated fromNational Chiao Tung University, Taiwan on Tuesday, April 27, 2021.
Acts like human brain
Basically,machine learningis the study of how computers can understand and act like the human brain through data.The goal is for the computer to gaininsightfrom the data.
The function ofmachinelearningas a computation platform for analyzing data is undoubtedly beneficial.There are manymachine learningalgorithmsthat can be used for data processing and analysis, especially when compared to traditional statistical methods.
For example, in applying the method to traditional statistics, there are two assumptions that must be fulfilled.However, withmachine learning being, it is more flexible, the use of any algorithm is just a matter of adjusting to existing data patterns.Furthermore, from various algorithms, the most optimal in accuracy, sensitivity, error value, and other model goodness criteria is selected.
“Machine learninghelps medical personnel get insights from data to make it faster and more accurate in diagnosing or identifying diseases,” explained Ratih.
Offers many processing algorithms
Machine learningoffers many algorithms that can be used to process data.These algorithms are divided intosupervised, unsupervised,andreinforcement learningwhere each type is further divided into several types of algorithms.For example, forsupervised learningknowndecision tree,support vector machine, neural networks, naive Bayes, and so forth.
“Ahealth scientistbefore applying algorithms to the data processing analysis also needs to understand the case at hand.Furthermore, having knowledge related toproblemsto be resolved is very necessary in addition to knowledge related tomachine learning,” she added.
The application ofmachine learningin the medical sector is like the detection of a disease, for example there is a lump in the skin, somachine learningcan detect whether the lump is a benign tumor or a malignant (cancer) tumor.In addition, in the field ofradiologists,machine learningcan help read visualizations of X-Ray results more quickly and accurately.
“Hopefully in the futuremachine learning willbe used more in processing data, so that it is more effective, efficient, maximal, and accurate.Moreover, it offers various methods andtoolsthat are easy to use in processing data, ” she concluded.(*)
Author: Wildan Suyuti
