【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

最新推荐文章于 2025-12-02 16:47:29 发布

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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:        - <int> max_iterations -> default = 200%   FactorAnalysis: - none%   GPLVM:          - <double> sigma -> default = 1.0%   Sammon:         - none%   Isomap:         - <int> k -> default = 12%   LandmarkIsomap: - <int> k -> default = 12%                   - <double> percentage -> default = 0.2%   LLE:            - <int> k -> default = 12%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   Laplacian:      - <int> k -> default = 12%                   - <double> sigma -> default = 1.0%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   HessianLLE:     - <int> k -> default = 12%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   LTSA:           - <int> k -> default = 12%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   MVU:            - <int> k -> default = 12%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   CCA:            - <int> k -> default = 12%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   LandmarkMVU:    - <int> k -> default = 5%   FastMVU:        - <int> k -> default = 5%                   - <logical> finetune -> default = true%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   DiffusionMaps:  - <double> t -> default = 1.0%                   - <double> sigma -> default = 1.0%   KernelPCA:      - <char[]> kernel -> {'linear', 'poly', ['gauss']} %                   - kernel parameters: type HELP GRAM for info%   GDA:            - <char[]> kernel -> {'linear', 'poly', ['gauss']} %                   - kernel parameters: type HELP GRAM for info%   SNE:            - <double> perplexity -> default = 30%   SymSNE:         - <double> perplexity -> default = 30%   tSNE:           - <int> initial_dims -> default = 30%                   - <double> perplexity -> default = 30%   LPP:            - <int> k -> default = 12%                   - <double> sigma -> default = 1.0%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   NPE:            - <int> k -> default = 12%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   LLTSA:          - <int> k -> default = 12%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   SPE:            - <char[]> type -> {['Global'], 'Local'}%                   - if 'Local': <int> k -> default = 12%   Autoencoder:    - <double> lambda -> default = 0%   LLC:            - <int> k -> default = 12%                   - <int> no_analyzers -> default = 20%                   - <int> max_iterations -> default = 200%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   ManifoldChart:  - <int> no_analyzers -> default = 40%                   - <int> max_iterations -> default = 200%                   - <char[]> eig_impl -> {['Matlab'], 'JDQR'}%   CFA:            - <int> no_analyzers -> default = 2%                   - <int> max_iterations -> default = 200%   NCA:            - <double> lambda -> default = 0.0%   MCML:           - none%   LMNN:           - <int> 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