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Machine learning: how to create a model for the prediction of diseases according to the symptoms of patients using the SVM algorithm

I have a matrix for diseases associated with their symptoms, the columns represent symptoms in each row is a disease. labels 1 and 0 in the matrix datasets represent when an illness I is related to specific symptoms j in the matrix[i, j] give 1 otherwise 0 if it is not related.
The objective of this work is to implement the SVM method to classify diseases and use the symptoms to generate patients to predict the most probable diseases. then calculating the performance of the precision, recovery and F1Score model.

GM = {{{1, 1, 0, 1, 1, 0, 0} ->
"rare disease", {0, 0, 0, 0, 0, 0, 0 ->
"rare disease", {0, 0, 0, 0, 0, 0, 0 ->
"rare disease", {0, 0, 0, 0, 0, 0, 0 ->
"rare disease", {1, 1, 1, 1, 0, 1, 1} ->
"rare disease", {0, 1, 1, 1, 0, 0, 0 ->
"rare disease", {1, 1, 0, 0, 0, 0, 1} ->
"rare disease", {0, 1, 0, 0, 0, 0, 1} ->
"rare disease", {0, 0, 1, 1, 0, 0, 1} ->
"rare disease", {0, 0, 0, 0, 0, 0, 0 ->
"rare disease", {0, 0, 0, 0, 0, 0, 0 ->
"rare disease", {0, 0, 0, 0, 0, 0, 0 ->
"rare disease", {1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0 ->
"Comon's disease", {0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1} ->
"Comon's disease", {0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1} ->
"Comon's disease", {0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1} ->
"Comon's disease", {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ->
"Comon's disease", {0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0} ->
"Comon's disease"}, {{1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1} ->
"Comon's disease", {0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1} ->
"Comon's disease", {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ->
"Comon's disease", {0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1} ->
"Comon's disease", {1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0 ->
"Comon's disease", {1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0 ->
"comon disease"}}

Here I tried to train and test the SVM classifier

{trainset, testset} = TakeDrop[GM, 18];
SVM = Sort[trainset, Method -> "SupportVectorMachine"]

(Then I created a new matrix for patients of Evry's disease in the origin data set)

Dsymp = Select[
  Table[{i, Position[GM[[i]], one]// Flatten}, {i, 1,
Dimensions[GM][[1]]}],[[2]]! = {}) &];

For more information, the previous output represents patients, which are a series of symptoms related to each disease in the womb.
I need to implement the SVM method to predict the most probable disease for each patient and test the performance
,

Here I generated a randomized set of 10 patients to predict the most likely diseases.

Samp = Table[Random[Integer, {1, Dimensions[Dsymp][[1]]}], {i, 1, 10}];

with I knew his illness

DTT1 = Dsymp[[Samp]];

Anyone can help me implement the SVM classifier.
The alternative question here enter the description of the link here.
Thank you for the information.