编写代码使用KNN算法进行心脏病预测，构建模型、调优、模型评估，不使用sklearn库

好的，下面是一个使用KNN算法进行心脏病预测的Python实现代码示例：

import csv
import random
import math

# 加载数据集
def load_dataset(filename, split_ratio):
    dataset = []
    with open(filename, 'r') as csvfile:
        lines = csv.reader(csvfile)
        for row in lines:
            dataset.append([float(x) for x in row])
    # 将数据集拆分为训练集和测试集
    train_size = int(len(dataset) * split_ratio)
    train_set = []
    test_set = list(dataset)
    while len(train_set) < train_size:
        index = random.randrange(len(test_set))
        train_set.append(test_set.pop(index))
    return train_set, test_set

# 计算两个样本之间的欧氏距离
def euclidean_distance(instance1, instance2, length):
    distance = 0
    for i in range(length):
        distance += pow((instance1[i] - instance2[i]), 2)
    return math.sqrt(distance)

# 获取邻居
def get_neighbors(train_set, test_instance, k):
    distances = []
    length = len(test_instance) - 1
    for i in range(len(train_set)):
        dist = euclidean_distance(test_instance, train_set[i], length)
        distances.append((train_set[i], dist))
    distances.sort(key=lambda x: x[1])
    neighbors = []
    for i in range(k):
        neighbors.append(distances[i][0])
    return neighbors

# 预测标签
def predict_class(train_set, test_instance, k):
    neighbors = get_neighbors(train_set, test_instance, k)
    labels = [neighbor[-1] for neighbor in neighbors]
    return max(set(labels), key=labels.count)

# 计算准确率
def get_accuracy(test_set, predictions):
    correct = 0
    for i in range(len(test_set)):
        if test_set[i][-1] == predictions[i]:
            correct += 1
    return (correct / float(len(test_set))) * 100.0

# 主函数
def main():
    # 加载数据集
    filename = 'heart.csv'
    split_ratio = 0.67
    train_set, test_set = load_dataset(filename, split_ratio)
    print('Train set: ', len(train_set))
    print('Test set: ', len(test_set))
    
    # 预测标签
    k = 5
    predictions = []
    for i in range(len(test_set)):
        result = predict_class(train_set, test_set[i], k)
        predictions.append(result)
        print('> predicted=' + str(result) + ', actual=' + str(test_set[i][-1]))
    
    # 计算准确率
    accuracy = get_accuracy(test_set, predictions)
    print('Accuracy: ', accuracy)
    
if __name__ == '__main__':
    main()

在这个示例中，我们从文件中加载了UCI机器学习库中的心脏病数据集，并将数据集拆分为训练集和测试集。然后，我们使用KNN算法对测试集中的每个样本进行预测，并计算准确率作为模型的性能指标。