Pythonで二値分類をする際にエラーメッセージがでる

Pythonを使用し、JupiterLab(3.44)でSignateの
【練習問題】健診データによる肝疾患判定を行っています。
LightGBMを使用して二値分類を行おうと思い、モデル作成のところまでは進んだのですが、
モデルを実行した際に以下のエラーメッセージが発生しました。

実現したいこと

ここに実現したいことを箇条書きで書いてください。
エラーが発生せず、モデルを学習することができる

発生している問題・エラーメッセージ

ValueError: DataFrame for label cannot have multiple columns
エラーメッセージ


### 該当のソースコード
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_988\2222427555.py in <module>
----> 1 model = lgb.train(params,
      2                   train_set = trains,
      3                   valid_sets = valids,
      4                   valid_names =["disease"],
      5                   num_boost_round=1000,

~\anaconda3\lib\site-packages\lightgbm\engine.py in train(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)
    269     # construct booster
    270     try:
--> 271         booster = Booster(params=params, train_set=train_set)
    272         if is_valid_contain_train:
    273             booster.set_train_data_name(train_data_name)

~\anaconda3\lib\site-packages\lightgbm\basic.py in __init__(self, params, train_set, model_file, model_str, silent)
   2603                 )
   2604             # construct booster object
-> 2605             train_set.construct()
   2606             # copy the parameters from train_set
   2607             params.update(train_set.get_params())

~\anaconda3\lib\site-packages\lightgbm\basic.py in construct(self)
   1813             else:
   1814                 # create train
-> 1815                 self._lazy_init(self.data, label=self.label,
   1816                                 weight=self.weight, group=self.group,
   1817                                 init_score=self.init_score, predictor=self._predictor,

~\anaconda3\lib\site-packages\lightgbm\basic.py in _lazy_init(self, data, label, reference, weight, group, init_score, predictor, silent, feature_name, categorical_feature, params)
   1476                                                                                              categorical_feature,
   1477                                                                                              self.pandas_categorical)
-> 1478         label = _label_from_pandas(label)
   1479 
   1480         # process for args

~\anaconda3\lib\site-packages\lightgbm\basic.py in _label_from_pandas(label)
    609     if isinstance(label, pd_DataFrame):
    610         if len(label.columns) > 1:
--> 611             raise ValueError('DataFrame for label cannot have multiple columns')
    612         if _get_bad_pandas_dtypes(label.dtypes):
    613             raise ValueError('DataFrame.dtypes for label must be int, float or bool')

ValueError: DataFrame for label cannot have multiple columns

```Python
ソースコード
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob
import seaborn as sns
import csv
%matplotlib inline
import sklearn.preprocessing as sp
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
import lightgbm as lgb
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score

tests = glob.glob("test.csv")
train = glob.glob("train.csv")

data_list = []
data_list.append(pd.read_csv(train[0],index_col=0))
df = pd.concat(data_list)

data_list2 = []
data_list2.append(pd.read_csv(tests[0],index_col=0))
df2 = pd.concat(data_list2)

drop_col = ["AG_ratio","Gender","TP","Alb","Age"]

y_train = df["disease"]
X_train = df.drop(drop_col,axis=1)
X_train = X_train.drop("disease",axis=1)
X_test = df2.drop(drop_col,axis=1)

trains = lgb.Dataset(y_train,X_train)
valids = lgb.Dataset(X_test)

params = {
    "task":"train",
    "boosting_type":"gbdt",
    "objective":"binary",
    "metric":"auc",
}

model = lgb.train(params,
                  train_set = trains,
                  valid_sets = valids,
                  valid_names =["disease"],
                  num_boost_round=1000,
                  early_stopping_rounds=100,
                 )
↑このモデルのところでエラーが出ます