机器学习-基于KNN和LMKNN的心脏病预测

本文主要是介绍机器学习-基于KNN和LMKNN的心脏病预测，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

一、简介和环境准备

knn一般指邻近算法。邻近算法，或者说K最邻近（KNN，K-NearestNeighbor）分类算法是数据挖掘分类技术中最简单的方法之一。而lmknn是局部均值k最近邻分类算法。

本次实验环境需要用的是Google Colab和Google Drive，文件后缀是.ipynb可以直接用。非常轻量不用安装配置啥的，前者是像notebooks的网页编辑器，后者是谷歌云盘，用来存储数据，大家自行搜索登录即可，让colab绑定上云盘。colab一段时间不操作就断连了。

先点击左边的文件图标，再点上面第三个图标绑定谷歌云盘。

from google.colab import drive
drive.mount('/content/drive')

然后运行该代码（第一次会弹出个框进行确认），之后就会发现文件列表多了一个drive文件夹，这下面就是你Google Drive的文件内容

然后就可以用里面的文件了，我准备了心脏故障分析的表格heartfailure_clinical_……csv，用于此次实验。存放位置见上图。

同时引入基本库

from sklearn.preprocessing import MinMaxScaler
import pandas as pd

二、算法解析

2.1 KNN算法

import scipy.spatial
from collections import Counterclass KNN:def __init__(self, k):self.k = kdef fit(self, X, y):self.X_train = Xself.y_train = ydef distance(self, X1, X2):return scipy.spatial.distance.euclidean(X1, X2)def predict(self, X_test):final_output = []for i in range(len(X_test)):d = []votes = []for j in range(len(X_train)):dist = scipy.spatial.distance.euclidean(X_train[j] , X_test[i])d.append([dist, j])d.sort()d = d[0:self.k]for d, j in d:votes.append(self.y_train[j])ans = Counter(votes).most_common(1)[0][0]final_output.append(ans)return final_outputdef score(self, X_test, y_test):predictions = self.predict(X_test)value = 0for i in range(len(y_test)):if(predictions[i] == y_test[i]):value += 1return value / len(y_test)

2.2LMKNN算法

import scipy.spatial
import numpy as np
from operator import itemgetterfrom collections import Counter
class LMKNN:def __init__(self, k):self.k = kdef fit(self, X, y):self.X_train = Xself.y_train = ydef distance(self, X1, X2):return scipy.spatial.distance.euclidean(X1, X2)def predict(self, X_test):final_output = []myclass = list(set(self.y_train))for i in range(len(X_test)):eucDist = []votes = []for j in range(len(X_train)):dist = scipy.spatial.distance.euclidean(X_train[j] , X_test[i])eucDist.append([dist, j, self.y_train[j]])eucDist.sort()minimum_dist_per_class = []for c in myclass:minimum_class = []for di in range(len(eucDist)):if(len(minimum_class) != self.k):if(eucDist[di][2] == c):minimum_class.append(eucDist[di])else:breakminimum_dist_per_class.append(minimum_class)indexData = []for a in range(len(minimum_dist_per_class)):temp_index = []for j in range(len(minimum_dist_per_class[a])):temp_index.append(minimum_dist_per_class[a][j][1])indexData.append(temp_index)centroid = []for a in range(len(indexData)):transposeData = X_train[indexData[a]].TtempCentroid = []for j in range(len(transposeData)):tempCentroid.append(np.mean(transposeData[j]))centroid.append(tempCentroid)centroid = np.array(centroid)eucDist_final = []for b in range(len(centroid)):dist = scipy.spatial.distance.euclidean(centroid[b] , X_test[i])eucDist_final.append([dist, myclass[b]])sorted_eucDist_final = sorted(eucDist_final, key=itemgetter(0))final_output.append(sorted_eucDist_final[0][1])return final_outputdef score(self, X_test, y_test):predictions = self.predict(X_test)value = 0for i in range(len(y_test)):if(predictions[i] == y_test[i]):value += 1return value / len(y_test)

2.2.1两种算法代码比对

两者唯一不同在于预测函数（predict）的不同，LMKNN的基本原理是在进行分类决策时，利用每个类中k个最近邻的局部平均向量对查询模式进行分类。可以看到加强局部模型的计算。最后处理结果的异同优异结尾有写。

2.3数据处理

2.3.1导入数据

train_path = r'drive/My Drive/Colab Notebooks/Dataset/heart_failure_clinical_records_dataset.csv'
data_train = pd.read_csv(train_path)
data_train.head()

检查有没有缺失的数据，查了下没有（都为0%）

for col in data_train.columns:print(col, str(round(100* data_train[col].isnull().sum() / len(data_train), 2)) + '%')

2.3.2数据预处理

data_train.drop('time',axis=1, inplace=True)
print(data_train.columns.tolist())

['age', 'anaemia', 'creatinine_phosphokinase', 'diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'DEATH_EVENT']

得到所有标签，放到后面的data_train 中

label_train = data_train['DEATH_EVENT'].to_numpy()
fitur_train = data_train[[ 'anaemia', 'creatinine_phosphokinase', 'diabetes', 'ejection_fraction', 'high_blood_pressure', 'platelets', 'serum_creatinine', 'serum_sodium']].to_numpy()
scaler = MinMaxScaler(feature_range=(0, 1))
scaler.fit(fitur_train)
fitur_train_normalize = scaler.transform(fitur_train)
print(fitur_train_normalize[0])

[0. 0.07131921 0. 0.09090909 1. 0.29082313 0.15730337 0.48571429]

2.4分类，令3K=3时

分别调用knn和lmknn训练，并输出准确率

from sklearn.model_selection import StratifiedKFold
kf = StratifiedKFold(n_splits=10, random_state=None, shuffle=True) 
kf.get_n_splits(fitur_train_normalize)acc_LMKNN_heart = [] 
acc_KNN_heart = [] 
for train_index, test_index in kf.split(fitur_train_normalize,label_train):knn = KNN(3)lmknn = LMKNN(3)X_train, X_test = fitur_train_normalize[train_index], fitur_train_normalize[test_index]y_train, y_test = label_train[train_index], label_train[test_index]knn.fit(X_train,y_train)prediction = knn.score(X_test, y_test)acc_KNN_heart.append(prediction)lmknn.fit(X_train, y_train) result = lmknn.score(X_test, y_test)acc_LMKNN_heart.append(result)
print(np.mean(acc_KNN_heart))
print(np.mean(acc_LMKNN_heart))

0.7157471264367816

0.7022988505747125

2.5对邻域大小K的敏感性结果

令k处于2-15调用看看，并输出所有结果。好像没有成功调用gpu，花了20s左右。

from sklearn.model_selection import StratifiedKFold
kf = StratifiedKFold(n_splits=10, random_state=None, shuffle=True) 
kf.get_n_splits(fitur_train_normalize)K = range(2,15)
result_KNN_HR = []
result_LMKNN_HR = []for k in K :acc_LMKNN_heart = [] acc_KNN_heart = [] for train_index, test_index in kf.split(fitur_train_normalize,label_train):knn = KNN(k)lmknn = LMKNN(k)X_train, X_test = fitur_train_normalize[train_index], fitur_train_normalize[test_index]y_train, y_test = label_train[train_index], label_train[test_index]knn.fit(X_train,y_train)prediction = knn.score(X_test, y_test)acc_KNN_heart.append(prediction)lmknn.fit(X_train, y_train) result = lmknn.score(X_test, y_test)acc_LMKNN_heart.append(result)result_KNN_HR.append(np.mean(acc_KNN_heart))result_LMKNN_HR.append(np.mean(acc_LMKNN_heart))
print('KNN : ',result_KNN_HR)
print('LMKNN : ',result_LMKNN_HR)

KNN : [0.6624137931034483, 0.7127586206896551, 0.7088505747126437, 0.7057471264367816, 0.689080459770115, 0.6886206896551724, 0.682528735632184, 0.6826436781609195, 0.69183908045977, 0.6791954022988506, 0.6722988505747127, 0.669080459770115, 0.6588505747126436]

LMKNN : [0.6922988505747126, 0.695977011494253, 0.6885057471264368, 0.6722988505747127, 0.6889655172413793, 0.6652873563218391, 0.6924137931034483, 0.6926436781609195, 0.7051724137931035, 0.6691954022988506, 0.689080459770115, 0.6857471264367816, 0.682528735632184]

生成图像，蓝色是knn，绿色是lmknn

import matplotlib.pyplot as plt
plt.plot(range(2,15), result_KNN_HR)
plt.plot(range(2,15), result_LMKNN_HR, color="green")
plt.ylabel('Accuracy')
plt.xlabel('K')
plt.show()