jun1-cs 님의 블로그

* terminologychromosome: the answer of question, form of output of modelgene: basic component of chromosomeencoding: form of answer -> chromosomedecoding: chromosome -> form of answer fitness: the score of how much each chromosome is fit to environment at that time point-> fitness function: use to get score of each chromosome Core concept of genetic algorithm - select chromosomes of high fitness..

1. Radiomics1) predicting binomial prognosis of glioblastoma with MRI imageMRI T1, T2 image -> use 1 axial image penetrating the center of tumor in each patient imagedata: 70 patient data consisting of clinical information and DICOM image -> segmentation of tumor using ImageJ-> define and calculate numercial features using radiomics package in R-> train and test ml model (Ex: Random Forest) - 0/..

1. kNN- select variables based on pca results- normalize (max-min / z norm) each variable- scatter datapoints into space- datapoints -> label O, label X group split- apply kNN alg -> classify label X group, using k nearest datapoints(label O group) * k is odd number! -> to escape the situation where k nearest datapoints show 50:50 proportion of two classes* supervised learning: must use labeled ..