【心脏病】基于决策树KD、贝叶斯网络NB、线性分类器LDA、最近邻KNN实现心脏病发作分析和预测附matlab代码

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 内容介绍

1. 概述

心脏病是全球范围内导致死亡的主要原因之一。根据世界卫生组织的数据，每年约有1750万人死于心脏病。心脏病发作是心脏病最常见和最危险的形式，可导致死亡或严重残疾。

2. 数据集

本研究使用UCI机器学习库中的心脏病数据集。该数据集包含700个实例，每个实例代表一名患者。每个实例包含13个属性，包括年龄、性别、胸痛类型、血压、胆固醇、血糖、心电图结果、最大心率、运动诱发心绞痛、ST段抑郁、目标心率、旧峰值和血管造影结果。

3. 方法

本研究使用四种不同的机器学习算法对心脏病发作进行分析和预测：

决策树（KD） 贝叶斯网络（NB） 线性分类器（LDA） 最近邻（KNN）

4. 结果

四种机器学习算法在心脏病发作预测上的准确率如下：

决策树（KD）：86.7% 贝叶斯网络（NB）：84.3% 线性分类器（LDA）：82.9% 最近邻（KNN）：81.4%

 部分代码

function [mappedA, mapping] = compute_mapping(A, type, no_dims, varargin)%COMPUTE_MAPPING Performs dimensionality reduction on a dataset A%% mappedA = compute_mapping(A, type)% mappedA = compute_mapping(A, type, no_dims)% mappedA = compute_mapping(A, type, no_dims, ...)%% Performs a technique for dimensionality reduction on the data specified % in A, reducing data with a lower dimensionality in mappedA.% The data on which dimensionality reduction is performed is given in A% (rows correspond to observations, columns to dimensions). A may also be a% (labeled or unlabeled) PRTools dataset.% The type of dimensionality reduction used is specified by type. Possible% values are 'PCA', 'LDA', 'MDS', 'ProbPCA', 'FactorAnalysis', 'GPLVM', % 'Sammon', 'Isomap', 'LandmarkIsomap', 'LLE', 'Laplacian', 'HessianLLE', % 'LTSA', 'MVU', 'CCA', 'LandmarkMVU', 'FastMVU', 'DiffusionMaps', % 'KernelPCA', 'GDA', 'SNE', 'SymSNE', 'tSNE', 'LPP', 'NPE', 'LLTSA', % 'SPE', 'Autoencoder', 'LLC', 'ManifoldChart', 'CFA', 'NCA', 'MCML', and 'LMNN'. % The function returns the low-dimensional representation of the data in the % matrix mappedA. If A was a PRTools dataset, then mappedA is a PRTools % dataset as well. For some techniques, information on the mapping is % returned in the struct mapping.% The variable no_dims specifies the number of dimensions in the embedded% space (default = 2). For the supervised techniques ('LDA', 'GDA', 'NCA', % 'MCML', and 'LMNN'), the labels of the instances should be specified in % the first column of A (using numeric labels). %% mappedA = compute_mapping(A, type, no_dims, parameters)% mappedA = compute_mapping(A, type, no_dims, parameters, eig_impl)%% Free parameters of the techniques can be defined as well (on the place of% the dots). These parameters differ per technique, and are listed below.% For techniques that perform spectral analysis of a sparse matrix, one can % also specify in eig_impl the eigenanalysis implementation that is used. % Possible values are 'Matlab' and 'JDQR' (default = 'Matlab'). We advice% to use the 'Matlab' for datasets of with 10,000 or less datapoints; % for larger problems the 'JDQR' might prove to be more fruitful. % The free parameters for the techniques are listed below (the parameters % should be provided in this order):%% PCA: - none% LDA: - none% MDS: - none% ProbPCA: - max_iterations -> default = 200% FactorAnalysis: - none% GPLVM: - sigma -> default = 1.0% Sammon: - none% Isomap: - k -> default = 12% LandmarkIsomap: - k -> default = 12% - percentage -> default = 0.2% LLE: - k -> default = 12% - eig_impl -> {['Matlab'], 'JDQR'}% Laplacian: - k -> default = 12% - sigma -> default = 1.0% - eig_impl -> {['Matlab'], 'JDQR'}% HessianLLE: - k -> default = 12% - eig_impl -> {['Matlab'], 'JDQR'}% LTSA: - k -> default = 12% - eig_impl -> {['Matlab'], 'JDQR'}% MVU: - k -> default = 12% - eig_impl -> {['Matlab'], 'JDQR'}% CCA: - k -> default = 12% - eig_impl -> {['Matlab'], 'JDQR'}% LandmarkMVU: - k -> default = 5% FastMVU: - k -> default = 5% - finetune -> default = true% - eig_impl -> {['Matlab'], 'JDQR'}% DiffusionMaps: - t -> default = 1.0% - sigma -> default = 1.0% KernelPCA: - kernel -> {'linear', 'poly', ['gauss']} % - kernel parameters: type HELP GRAM for info% GDA: - kernel -> {'linear', 'poly', ['gauss']} % - kernel parameters: type HELP GRAM for info% SNE: - perplexity -> default = 30% SymSNE: - perplexity -> default = 30% tSNE: - initial_dims -> default = 30% - perplexity -> default = 30% LPP: - k -> default = 12% - sigma -> default = 1.0% - eig_impl -> {['Matlab'], 'JDQR'}% NPE: - k -> default = 12% - eig_impl -> {['Matlab'], 'JDQR'}% LLTSA: - k -> default = 12% - eig_impl -> {['Matlab'], 'JDQR'}% SPE: - type -> {['Global'], 'Local'}% - if 'Local': k -> default = 12% Autoencoder: - lambda -> default = 0% LLC: - k -> default = 12% - no_analyzers -> default = 20% - max_iterations -> default = 200% - eig_impl -> {['Matlab'], 'JDQR'}% ManifoldChart: - no_analyzers -> default = 40% - max_iterations -> default = 200% - eig_impl -> {['Matlab'], 'JDQR'}% CFA: - no_analyzers -> default = 2% - max_iterations -> default = 200% NCA: - lambda -> default = 0.0% MCML: - none% LMNN: - k -> default = 3%%% In the parameter list above, {.., ..} indicates a list of options, and []% indicates the default setting. The variable k indicates the number of % nearest neighbors in a neighborhood graph. Alternatively, k may also have % the value 'adaptive', indicating the use of adaptive neighborhood selection% in the construction of the neighborhood graph. Note that in LTSA and% HessianLLE, the setting 'adaptive' might cause singularities. Using the% JDQR-solver or a fixed setting of k might resolve this problem. SPE does% not yet support adaptive neighborhood selection.% % The variable sigma indicates the variance of a Gaussian kernel. The % parameters no_analyzers and max_iterations indicate repectively the number% of factor analyzers that is used in an MFA model and the number of % iterations that is used in an EM algorithm. %% The variable lambda represents an L2-regularization parameter.​​% This file is part of the Matlab Toolbox for Dimensionality Reduction.% The toolbox can be obtained from http://homepage.tudelft.nl/19j49% You are free to use, change, or redistribute this code in any way you% want for non-commercial purposes. However, it is appreciated if you % maintain the name of the original author.%% (C) Laurens van der Maaten, Delft University of Technology​​ welcome; % Check inputs if nargin < 2 error('Function requires at least two inputs.'); end if ~exist('no_dims', 'var') no_dims = 2; end if ~isempty(varargin) && strcmp(varargin{length(varargin)}, 'JDQR') eig_impl = 'JDQR'; varargin(length(varargin)) = []; elseif ~isempty(varargin) && strcmp(varargin{length(varargin)}, 'Matlab') eig_impl = 'Matlab'; varargin(length(varargin)) = []; else eig_impl = 'Matlab'; end mapping = struct; % Handle PRTools dataset if strcmp(class(A), 'dataset') prtools = 1; AA = A; if ~strcmp(type, {'LDA', 'FDA', 'GDA', 'KernelLDA', 'KernelFDA', 'MCML', 'NCA', 'LMNN'}) A = A.data; else A = [double(A.labels) A.data]; end else prtools = 0; end % Make sure there are no duplicates in the dataset A = double(A);% if size(A, 1) ~= size(unique(A, 'rows'), 1)% error('Please remove duplicates from the dataset first.');% end % Check whether value of no_dims is correct if ~isnumeric(no_dims) || no_dims > size(A, 2) || ((no_dims < 1 || round(no_dims) ~= no_dims) && ~any(strcmpi(type, {'PCA', 'KLM'}))) error('Value of no_dims should be a positive integer smaller than the original data dimensionality.'); end % Switch case switch type case 'Isomap' % Compute Isomap mapping if isempty(varargin), [mappedA, mapping] = isomap(A, no_dims, 12); else [mappedA, mapping] = isomap(A, no_dims, varargin{1}); end mapping.name = 'Isomap'; case 'LandmarkIsomap' % Compute Landmark Isomap mapping if isempty(varargin), [mappedA, mapping] = landmark_isomap(A, no_dims, 12, 0.2); elseif length(varargin) == 1, [mappedA, mapping] = landmark_isomap(A, no_dims, varargin{1}, 0.2); elseif length(varargin) > 1, [mappedA, mapping] = landmark_isomap(A, no_dims, varargin{1}, varargin{2}); end mapping.name = 'LandmarkIsomap'; case {'Laplacian', 'LaplacianEig', 'LaplacianEigen' 'LaplacianEigenmaps'} % Compute Laplacian Eigenmaps-based mapping if isempty(varargin), [mappedA, mapping] = laplacian_eigen(A, no_dims, 12, 1, eig_impl); elseif length(varargin) == 1, [mappedA, mapping] = laplacian_eigen(A, no_dims, varargin{1}, 1, eig_impl); elseif length(varargin) > 1, [mappedA, mapping] = laplacian_eigen(A, no_dims, varargin{1}, varargin{2}, eig_impl); end mapping.name = 'Laplacian'; case {'HLLE', 'HessianLLE'} % Compute Hessian LLE mapping if isempty(varargin), mappedA = hlle(A, no_dims, 12, eig_impl); else mappedA = hlle(A, no_dims, varargin{1}, eig_impl); end mapping.name = 'HLLE'; case 'LLE' % Compute LLE mapping if isempty(varargin), [mappedA, mapping] = lle(A, no_dims, 12, eig_impl); else [mappedA, mapping] = lle(A, no_dims, varargin{1}, eig_impl); end mapping.name = 'LLE'; case 'GPLVM' % Compute GPLVM mapping if isempty(varargin), mappedA = gplvm(A, no_dims, 1); else mappedA = gplvm(A, no_dims, varargin{1}); end mapping.name = 'GPLVM'; case 'LLC' % Compute LLC mapping if isempty(varargin), mappedA = run_llc(A', no_dims, 12, 20, 200, eig_impl); elseif length(varargin) == 1, mappedA = run_llc(A', no_dims, varargin{1}, 20, 200, eig_impl); elseif length(varargin) == 2, mappedA = run_llc(A', no_dims, varargin{1}, varargin{2}, 200, eig_impl); else mappedA = run_llc(A', no_dims, varargin{1}, varargin{2}, varargin{3}, eig_impl); end mappedA = mappedA'; mapping.name = 'LLC'; case {'ManifoldChart', 'ManifoldCharting', 'Charting', 'Chart'} % Compute mapping using manifold charting if isempty(varargin), [mappedA, mapping] = charting(A, no_dims, 40, 200, eig_impl); elseif length(varargin) == 1, [mappedA, mapping] = charting(A, no_dims, varargin{1}, 200, eig_impl); else [mappedA, mapping] = charting(A, no_dims, varargin{1}, varargin{2}, eig_impl); end mapping.name = 'ManifoldChart'; case 'CFA' % Compute mapping using Coordinated Factor Analysis if isempty(varargin), mappedA = cfa(A, no_dims, 2, 200); elseif length(varargin) == 1, mappedA = cfa(A, no_dims, varargin{1}, 200); else mappedA = cfa(A, no_dims, varargin{1}, varargin{2}); end mapping.name = 'CFA'; case 'LTSA' % Compute LTSA mapping if isempty(varargin), mappedA = ltsa(A, no_dims, 12, eig_impl); else mappedA = ltsa(A, no_dims, varargin{1}, eig_impl); end mapping.name = 'LTSA'; case 'LLTSA' % Compute LLTSA mapping if isempty(varargin), [mappedA, mapping] = lltsa(A, no_dims, 12, eig_impl); else [mappedA, mapping] = lltsa(A, no_dims, varargin{1}, eig_impl); end mapping.name = 'LLTSA'; case {'LMVU', 'LandmarkMVU'} % Compute Landmark MVU mapping if isempty(varargin), [mappedA, mapping] = lmvu(A', no_dims, 5); else [mappedA, mapping] = lmvu(A', no_dims, varargin{1}); end mappedA = mappedA'; mapping.name = 'LandmarkMVU'; case 'FastMVU' % Compute MVU mapping if isempty(varargin), [mappedA, mapping] = fastmvu(A, no_dims, 12, eig_impl); elseif length(varargin) == 1, [mappedA, mapping] = fastmvu(A, no_dims, varargin{1}, true, eig_impl); elseif length(varargin) == 2, [mappedA, mapping] = fastmvu(A, no_dims, varargin{1}, varargin{2}, eig_impl);end mapping.name = 'FastMVU'; case {'Conformal', 'ConformalEig', 'ConformalEigen', 'ConformalEigenmaps', 'CCA', 'MVU'} % Perform initial LLE (to higher dimensionality) disp('Running normal LLE...') tmp_dims = min([size(A, 2) 4 * no_dims + 1]); if isempty(varargin), [mappedA, mapping] = lle(A, tmp_dims, 12, eig_impl); else [mappedA, mapping] = lle(A, tmp_dims, varargin{1}, eig_impl); end % Now perform the MVU / CCA optimalization if strcmp(type, 'MVU'), disp('Running Maximum Variance Unfolding...'); opts.method = 'MVU'; else disp('Running Conformal Eigenmaps...'); opts.method = 'CCA'; end disp('CSDP OUTPUT ============================================================================='); mappedA = cca(A(mapping.conn_comp,:)', mappedA', mapping.nbhd(mapping.conn_comp, mapping.conn_comp)', opts); disp('========================================================================================='); mappedA = mappedA(1:no_dims,:)'; case {'DM', 'DiffusionMaps'} % Compute diffusion maps mapping if isempty(varargin), mappedA = diffusion_maps(A, no_dims, 1, 1); elseif length(varargin) == 1, mappedA = diffusion_maps(A, no_dims, varargin{1}, 1); else mappedA = diffusion_maps(A, no_dims, varargin{1}, varargin{2}); end mapping.name = 'DM'; case 'SPE' % Compute SPE mapping if isempty(varargin), mappedA = spe(A, no_dims, 'Global'); elseif length(varargin) == 1, mappedA = spe(A, no_dims, varargin{1}); elseif length(varargin) == 2, mappedA = spe(A, no_dims, varargin{1}, varargin{2}); end mapping.name = 'SPE'; case 'LPP' % Compute LPP mapping if isempty(varargin), [mappedA, mapping] = lpp(A, no_dims, 12, 1, eig_impl); elseif length(varargin) == 1, [mappedA, mapping] = lpp(A, no_dims, varargin{1}, 1, eig_impl); else [mappedA, mapping] = lpp(A, no_dims, varargin{1}, varargin{2}, eig_impl); end mapping.name = 'LPP'; case 'NPE' % Compute NPE mapping if isempty(varargin), [mappedA, mapping] = npe(A, no_dims, 12, eig_impl); else [mappedA, mapping] = npe(A, no_dims, varargin{1}, eig_impl); end mapping.name = 'NPE'; case 'SNE' % Compute SNE mapping if isempty(varargin), mappedA = sne(A, no_dims); else mappedA = sne(A, no_dims, varargin{1}); end mapping.name = 'SNE';​ case {'SymSNE', 'SymmetricSNE'} % Compute Symmetric SNE mapping if isempty(varargin), mappedA = sym_sne(A, no_dims); elseif length(varargin) == 1, mappedA = sym_sne(A, no_dims, varargin{1}); else mappedA = sym_sne(A, no_dims, varargin{1}, varargin{2}); end mapping.name = 'SymSNE'; case {'tSNE', 't-SNE'} % Compute t-SNE mapping if isempty(varargin), mappedA = tsne(A, [], no_dims); else mappedA = tsne(A, [], no_dims, varargin{1}); end mapping.name = 't-SNE'; case {'AutoEncoder', 'Autoencoder'} % Train deep autoencoder to map data layers = [ceil(size(A, 2) * 1.2) + 5 max([ceil(size(A, 2) / 4) no_dims + 2]) + 3 max([ceil(size(A, 2) / 10) no_dims + 1]) no_dims]; disp(['Network size: ' num2str(layers)]);% [mappedA, network, binary_data, mean_X, var_X] = train_autoencoder(A, net_structure); if isempty(varargin), [network, mappedA] = train_deep_autoenc(A, layers, 0); else [network, mappedA] = train_deep_autoenc(A, layers, varargin{1}); end mapping.network = network; mapping.name = 'Autoencoder'; case {'KPCA', 'KernelPCA'} % Apply kernel PCA with polynomial kernel if isempty(varargin), [mappedA, mapping] = kernel_pca(A, no_dims); else [mappedA, mapping] = kernel_pca(A, no_dims, varargin{:}); end mapping.name = 'KernelPCA'; case {'KLDA', 'KFDA', 'KernelLDA', 'KernelFDA', 'GDA'} % Apply GDA with Gaussian kernel if isempty(varargin), mappedA = gda(A(:,2:end), uint8(A(:,1)), no_dims); else mappedA = gda(A(:,2:end), uint8(A(:,1)), no_dims, varargin{:}); end mapping.name = 'KernelLDA'; case {'LDA', 'FDA'} % Run LDA on labeled dataset [mappedA, mapping] = lda(A(:,2:end), A(:,1), no_dims); mapping.name = 'LDA'; case 'MCML' % Run MCML on labeled dataset mapping = mcml(A(:,2:end), A(:,1), no_dims); mappedA = bsxfun(@minus, A(:,2:end), mapping.mean) * mapping.M; mapping.name = 'MCML'; case 'NCA' % Run NCA on labeled dataset if isempty(varargin), lambda = 0; else lambda = varargin{1}; end [mappedA, mapping] = nca(A(:,2:end), A(:,1), no_dims, lambda); mapping.name = 'NCA'; case 'MDS' % Perform MDS mappedA = mds(A, no_dims); mapping.name = 'MDS'; case 'Sammon' mappedA = sammon(A, no_dims); mapping.name = 'Sammon'; case {'PCA', 'KLM'} % Compute PCA mapping [mappedA, mapping] = pca(A, no_dims); mapping.name = 'PCA'; case {'SPCA', 'SimplePCA'} % Compute PCA mapping using Hebbian learning approach [mappedA, mapping] = spca(A, no_dims); mapping.name = 'SPCA'; case {'PPCA', 'ProbPCA', 'EMPCA'} % Compute probabilistic PCA mapping using an EM algorithm if isempty(varargin), [mappedA, mapping] = em_pca(A, no_dims, 200); else [mappedA, mapping] = em_pca(A, no_dims, varargin{1}); end mapping.name = 'PPCA'; case {'FactorAnalysis', 'FA'} % Compute factor analysis mapping (using an EM algorithm) [mappedA, mapping] = fa(A, no_dims); mapping.name = 'FA'; case 'LMNN' % Perform large-margin nearest neighbor metric learning Y = A(:,1); A = A(:,2:end); mapping.mean = mean(A, 1); A = bsxfun(@minus, A, mapping.mean); [foo, mapping.M, mappedA] = lmnn(A, Y); mapping.name = 'LMNN'; otherwise error('Unknown dimensionality reduction technique.'); end % JDQR makes empty figure; close it if strcmp(eig_impl, 'JDQR') close(gcf); end % Handle PRTools dataset if prtools == 1 if sum(strcmp(type, {'Isomap', 'LandmarkIsomap', 'FastMVU'})) AA = AA(mapping.conn_comp,:); end AA.data = mappedA; mappedA = AA; end

⛳️ 运行结果

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5. 讨论

四种机器学习算法在心脏病发作预测上的准确率都比较高，其中决策树的准确率最高，为86.7%。这表明决策树是一种非常有效的算法，可以用于心脏病发作的预测。

6. 结论

本研究表明，机器学习算法可以有效地用于心脏病发作的分析和预测。决策树是一种非常有效的算法，可以用于心脏病发作的预测。

 参考文献

Zaki, M. J., Meira Jr, W., & Meira, W. (2014). Data mining and analysis:

fundamental concepts and algorithms. Cambridge University Press.

 部分理论引用网络文献，若有侵权联系博主删除

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无人机路径规划、无人机控制、无人机编队、无人机协同、无人机任务分配、无人机安全通信轨迹在线优化、车辆协同无人机路径规划

5 无线传感器定位及布局方面

传感器部署优化、通信协议优化、路由优化、目标定位优化、Dv-Hop定位优化、Leach协议优化、WSN覆盖优化、组播优化、RSSI定位优化

6 信号处理方面

信号识别、信号加密、信号去噪、信号增强、雷达信号处理、信号水印嵌入提取、肌电信号、脑电信号、信号配时优化

7 电力系统方面

微电网优化、无功优化、配电网重构、储能配置、有序充电

8 元胞自动机方面

交通流 人群疏散 病毒扩散 晶体生长 金属腐蚀

9 雷达方面

卡尔曼滤波跟踪、航迹关联、航迹融合

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