univer-5th-semester/literature-review
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classificators in results

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Артём Тищенко
2024-11-06 13:24:32 +03:00
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@@ -177,7 +177,16 @@
% \section{Feature analysis}
% \section{Results}
\section{Results}
In all works, the construction of machine learning models is essentially a secondary result. First of all, studies show the applicability of these methods to tasks related to the problems of cancer cell resistance to chemotherapy. Also, using machine learning methods, the authors test their hypotheses, confirm or discover links between various characteristics of cancer cells, patient clinical data and drug resistance.
In articles \cite{paclitaxel}, \cite{sers}, \cite{platinum}, \cite{kras}, the authors try to solve the problem of determining drug resistance directly. In \cite{sers}, \cite{platinum}, \cite{kras}, the problem of binary classification (drug resistant vs drug sensitive) is solved, and in \cite{paclitaxel}, cells are classified into 4 classes, which constitute a gradation of the level of resistance of cancer cells to chemotherapy.
In \cite{paclitaxel}, five different machine learning algorithms were compared, the best results were achieved using support vector machine (accuracy of 93.4\%) and neural network (accuracy of 94.5\%). The classification was based on morphological features and, by constructing effective classifiers, the authors demonstrated that these features are directly related to the level of resistance of cancer cells to chemotherapy. Also, using SHapley Additive exPlanations authors showed that only a 25 of 112 features are really important for the classification.
The authors of \cite{sers}, applied robust machine learning algorithm based on principal component analysis and linear discriminant analysis and established an effective predictive model with the accuracy of 96.7\% for identifying the radiotherapy resistance subjects from sensitivity ones, and 100\% for identifying the NPC subjects from healthy ones. Also authors showed the importance of the separation of plasma into upper and lower plasma by comparing model results, e. g. for upper plasma and radiotherapy resistance vs. radiotherapy sensitivity classification task their model achieved 98.7\% accuracy while for lower plasma it is only at level of 93.9\%.
In \cite{kras}, authors firstly applied machine learning algorithms to extract most important features and created seven feature lists, after that they applied four classification algorithms. Their best result was achieved with CATBoost feature list and support vector machine as classification algorithms (accuracy of 93.1\%). Also after analysing recieved feature lists authors were able to identify top genes associated with tumor progression and drug resistance (H2AFZ, CKS1B, TUBA1B, RRM2, BIRC5).
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