From eb13f542af6658474e0f646b6803d8248d9958ba Mon Sep 17 00:00:00 2001 From: dd5124 Date: Mon, 16 Dec 2024 00:40:33 -0800 Subject: [PATCH] replace hard coded values with inline code --- .../figure-html/cell-36-output-1.png | Bin 0 -> 16260 bytes .../figure-html/cell-38-output-1.png | Bin 0 -> 110180 bytes .../figure-html/cell-38-output-2.png | Bin 0 -> 354276 bytes .../figure-html/cell-50-output-1.png | Bin 0 -> 14030 bytes .../figure-html/cell-55-output-1.png | Bin 0 -> 82011 bytes index.html | 53 +++++++------- report/content/_analysis.qmd | 66 +++++++++++++----- report/content/_discussion.qmd | 24 ++++--- report/heart_disease_predictor_report.html | 53 +++++++------- report/heart_disease_predictor_report.pdf | Bin 797212 -> 796727 bytes .../feature_densities_by_diagnosis.png | Bin 546371 -> 547200 bytes results/models/decision_tree.pkl | Bin 7311 -> 7277 bytes results/models/logistic_regression.pkl | Bin 2045 -> 2045 bytes .../decision_tree_cv_results.csv | 4 +- 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z-kn#~&V4Osin$<{LZcX=⁢Wq1(6SNmnFaVkSr;5@7NZxX+DC{?sx$I_R8Q& zk;lK2I!G3l09lEt7PDBGaP`EZb8$g@*-pD&Dn_bD-P2nwoP1%0>aTkXRfD zF!xrw#Fd!iNsv8{Zkd0xD{~wtR>HhZs91f6JX44<{|?@ zd>_>rKb{F4Ql?crd7))d_z!%>>s~~nlLC>5ar6A5(K7s|@TmRDjnc!0xOzDy1xZhR{rmgo8}|R3`1D0gK6IuXDs_OywPJ&{MOVbX zg5^BIeU-Jxpy8Egf8w4 - + - + @@ -3110,7 +3105,7 @@

  2. References
  3. -

    Other Formats

    +

    Other Formats

    @@ -3492,7 +3487,7 @@

    EDA

    - +
    Figure 3: Feature Densities by Diagnosis @@ -3526,13 +3521,13 @@

    Dec fit_time -0.001 -0 +0.002 +0.002 score_time +0.006 0.002 -0 test_accuracy @@ -3581,7 +3576,7 @@

    Dec

    -

    Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with a test accuracy of 0.74, suggesting good generalization. The model exhibits a precision of 0.709, recall of 0.737, and an F1-score of 0.718 on the test set, with relatively low variability (standard deviations of 0.059, 0.069, and 0.079, respectively). These results indicate a reasonable trade-off between precision and recall.

    +

    Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with a test accuracy of 74.0%, suggesting good generalization. The model exhibits a precision of 70.9%, recall of 73.7%, and an F1-score of 71.8% on the test set, with relatively low variability. These results indicate a reasonable trade-off between precision and recall.

    @@ -3592,7 +3587,7 @@

    Dec

    -

    Confusion matrix reveals a higher number of false positives (29) compared to false negatives (25), indicating some misclassification of the negative class (0).

    +

    Confusion matrix reveals a higher number of false positives compared to false negatives, indicating some misclassification of the negative class (target = 0).

    Decision Tree: Test Results

    @@ -3639,7 +3634,7 @@

    Decision Tree: -

    Test results show balanced performance across classes, with precision for class 0 (0.727) and class 1 (0.771), and F1-scores of 0.777 and 0.701 for class 0 and 1, respectively. The model’s overall accuracy is 0.744.

    +

    Test results show balanced performance across classes, with precision for class 0 (72.73%) and class 1 (77.14%), and F1-scores of 77.67% and 70.13% for class 0 and 1, respectively. The model’s overall accuracy is 74.44%.

    Logistic Regression: Cross-Validation Results

    @@ -3662,12 +3657,12 @@

    fit_time -0.002 -0.001 +0.007 +0.003 score_time -0.002 +0.004 0 @@ -3717,7 +3712,7 @@

    @@ -3728,32 +3723,32 @@

    Logistic Regression: Coefficients

    - +
    Figure 6: Coefficients of Logistic Regression Model
    -

    The coefficients of the Logistic Regression model reflect the impact of each feature on the likelihood of a positive outcome, with positive coefficients indicating an increased likelihood and negative coefficients indicating a decreased likelihood. For example, features like chest_pain_type_asymptomatic (1.18), thalassemia_reversable defect (0.97), and num_of_vessels_2.0 (0.91) are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of a positive outcome. In contrast, features such as num_of_vessels_0.0 (-1.26), chest_pain_type_non-anginal pain (-0.93), and thalassemia_normal (-0.58) show a negative relationship, meaning they decrease the likelihood of heart disease. The coefficients for scaled features like age, cholesterol, and resting_blood_pressure (e.g., 0.34 for cholesterol) indicate their contribution to the prediction, with the magnitude of the coefficient reflecting the strength of their influence on the model’s outcome. Features with larger absolute coefficient values, such as num_of_vessels_0.0 and chest_pain_type_asymptomatic, have a more significant impact on the model’s prediction.

    +

    The coefficients of the Logistic Regression model reflect the impact of each feature on the likelihood of a positive outcome, with positive coefficients indicating an increased likelihood and negative coefficients indicating a decreased likelihood. For example, features like chest_pain_type_asymptomatic (1.18) are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of a positive outcome (presence of hear disease). In contrast, features such as num_of_vessels_0.0% (-1.26) show a negative relationship, meaning they decrease the likelihood of heart disease.

    Logistic Regression: Test Results

    -
    -
    +
    +
    Table 6: Test results of Logistic Regression Model
    -
    +
    @@ -3789,7 +3784,7 @@

    Logistic -

    Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (0.844). For precision and recall, class 0 achieves precision of 0.827 and recall of 0.896, while class 1 has precision of 0.868 and recall of 0.786, leading to an F1-score of 0.86 for class 0 and 0.825 for class 1.

    +

    Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (84.44%). For precision and recall, class 0 achieves precision of 82.69% and recall of 89.58%, while class 1 has precision of 86.84% and recall of 78.57%, leading to an F1-score of 82.5% for class 0 and 82.5% for class 1.

    @@ -3801,8 +3796,8 @@

    Results

    This analysis aimed to evaluate the accuracy and interpretability of machine learning models in predicting heart disease. Specifically, we sought to determine the (1) overall accuracy of classification models, (2) identify key predictive features, and (3) assess the ability of these models to predict the likelihood of a patient developing heart disease based on health indicators and demographic factors.

    Overall Accuracy of the Classification Models

    -

    Both the logistic regression and decision tree models demonstrated strong predictive capabilities. The decision tree model achieved an accuracy of 73.33%, with a precision of 72.22% and recall of 81.25% for class 0 (absence of heart disease) and a precision of 75.0% and recall of 64.29% for class 1 (presence of heart disease). However, the decision tree model exhibited signs of potential overfitting, as indicated by the 100% training accuracy versus a test accuracy of 72.5%, suggesting that it might not generalize well to unseen data.

    -

    In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of 81.11%. It exhibited balanced precision (78.18% for class 0 and 85.71% for class 1) and recall (89.58% for class 0 and 71.43% for class 1), with a test accuracy of 87.5%, and a training accuracy of 89.6%. This higher performance suggests that logistic regression provides a more reliable model for predicting heart disease, with fewer issues related to overfitting and better generalization to new data.

    +

    Both the logistic regression and decision tree models demonstrated strong predictive capabilities. The decision tree model achieved an accuracy of 74.44%, with a precision of 72.73% and recall of 83.33% for class 0 (absence of heart disease) and a precision of 77.14% and recall of 64.29% for class 1 (presence of heart disease). However, the decision tree model exhibited signs of potential overfitting, as indicated by the 100.0% training accuracy versus a test accuracy of 74.44%, suggesting that it might not generalize well to unseen data.

    +

    In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of 84.44%. It exhibited balanced precision (82.69% for class 0 and 86.84% for class 1) and recall (89.58% for class 0 and 78.57% for class 1), with a test accuracy of 84.44%, and a training accuracy of 87.3%. This higher performance suggests that logistic regression provides a more reliable model for predicting heart disease, with fewer issues related to overfitting and better generalization to new data.

    Key Predictive Features for Heart Disease

    @@ -3811,7 +3806,7 @@

    Predictive Capability for Patient Diagnosis

    -

    The ability to predict whether an individual might develop heart disease based on health indicators and demographic factors was evaluated through both models. Logistic regression demonstrated a strong capability to predict the likelihood of heart disease, with particularly high recall for the absence of heart disease (89.58%), meaning the model was effective in identifying healthy individuals. Conversely, the decision tree model showed a more balanced performance but struggled to consistently predict class 1 (heart disease), as reflected by its lower recall (64.29%) for this group.

    +

    The ability to predict whether an individual might develop heart disease based on health indicators and demographic factors was evaluated through both models. Logistic regression demonstrated a strong capability to predict the likelihood of heart disease, with particularly high recall for the absence of heart disease (78.57%), meaning the model was effective in identifying healthy individuals. Conversely, the decision tree model showed a more balanced performance but struggled to consistently predict class 1 (heart disease), as reflected by its lower recall (64.29%) for this group.

    Overall, the findings suggest that machine learning models, particularly logistic regression, hold promise for accurately predicting heart disease diagnoses. However, there is still room for improvement in refining these models to reduce misclassifications, especially in predicting the presence of heart disease.

    diff --git a/report/content/_analysis.qmd b/report/content/_analysis.qmd index 3395a0a..1ce245f 100644 --- a/report/content/_analysis.qmd +++ b/report/content/_analysis.qmd @@ -17,19 +17,22 @@ if 'Unnamed: 0' in dst_cv_results.columns: dt_test_accuracy_cv = round(dst_cv_results.loc[dst_cv_results[''] == 'test_accuracy', 'mean'].values[0] * 100, 2) dt_train_accuracy_cv = round(dst_cv_results.loc[dst_cv_results[''] == 'train_accuracy', 'mean'].values[0] * 100, 2) +dt_test_recall_cv = round(dst_cv_results.loc[dst_cv_results[''] == 'test_recall', 'mean'].values[0] * 100, 2) +dt_test_precision_cv = round(dst_cv_results.loc[dst_cv_results[''] == 'test_precision', 'mean'].values[0] * 100, 2) +dt_test_f1_cv = round(dst_cv_results.loc[dst_cv_results[''] == 'test_f1', 'mean'].values[0] * 100, 2) Markdown(dst_cv_results.to_markdown(index = False)) ``` -Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with a test accuracy of 0.74, -suggesting good generalization. The model exhibits a precision of 0.709, recall of 0.737, and an F1-score of 0.718 on the test set, -with relatively low variability (standard deviations of 0.059, 0.069, and 0.079, respectively). -These results indicate a reasonable trade-off between precision and recall. +Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with +a test accuracy of `{python} dt_test_accuracy_cv`%, suggesting good generalization. The model exhibits a precision of `{python} dt_test_precision_cv`%, +recall of `{python} dt_test_recall_cv`%, and an F1-score of `{python} dt_test_f1_cv`% on the test set, +with relatively low variability. These results indicate a reasonable trade-off between precision and recall. ![Confusion Matrix of Decision Tree Model](../results/tables/decision_tree/decision_tree_confusion_matrix.png){#fig-conf-m-dt} -Confusion matrix reveals a higher number of false positives (29) compared to false negatives (25), -indicating some misclassification of the negative class (0). +Confusion matrix reveals a higher number of false positives compared to false negatives, +indicating some misclassification of the negative class (target = 0). ### Decision Tree: Test Results @@ -38,14 +41,20 @@ indicating some misclassification of the negative class (0). #| tbl-cap: "Test results of Decision Tree Model" dt_precision_class_0 = round(dst_model_results.loc[dst_model_results[''] == '0', 'precision'].values[0] * 100, 2) dt_recall_class_0 = round(dst_model_results.loc[dst_model_results[''] == '0', 'recall'].values[0] * 100, 2) +dt_f1_class_0 = round(dst_model_results.loc[dst_model_results[''] == '0', 'f1-score'].values[0] * 100, 2) + dt_precision_class_1 = round(dst_model_results.loc[dst_model_results[''] == '1', 'precision'].values[0] * 100, 2) dt_recall_class_1 = round(dst_model_results.loc[dst_model_results[''] == '1', 'recall'].values[0] * 100, 2) +dt_f1_class_1 = round(dst_model_results.loc[dst_model_results[''] == '1', 'f1-score'].values[0] * 100, 2) + +dt_accuracy_test = round(dst_model_results.loc[dst_model_results[''] == 'accuracy', 'precision'].values[0] * 100, 2) Markdown(dst_model_results.to_markdown(index = False)) ``` -Test results show balanced performance across classes, with precision for class 0 (0.727) and class 1 (0.771), -and F1-scores of 0.777 and 0.701 for class 0 and 1, respectively. The model's overall accuracy is 0.744. +Test results show balanced performance across classes, with precision for class 0 (`{python} dt_precision_class_0`%) +and class 1 (`{python} dt_precision_class_1`%), and F1-scores of `{python} dt_f1_class_0`% and `{python} dt_f1_class_1`% +for class 0 and 1, respectively. The model's overall accuracy is `{python} dt_accuracy_test`%. ### Logistic Regression: Cross-Validation Results ```{python} @@ -58,28 +67,40 @@ if 'Unnamed: 0' in lg_cv_results.columns: lg_test_accuracy_cv = round(lg_cv_results.loc[lg_cv_results[''] == 'test_accuracy', 'mean'].values[0] * 100, 2) lg_train_accuracy_cv = round(lg_cv_results.loc[lg_cv_results[''] == 'train_accuracy', 'mean'].values[0] * 100, 2) +lg_test_recall_cv = round(lg_cv_results.loc[lg_cv_results[''] == 'test_recall', 'mean'].values[0] * 100, 2) +lg_test_precision_cv = round(lg_cv_results.loc[lg_cv_results[''] == 'test_precision', 'mean'].values[0] * 100, 2) +lg_test_f1_cv = round(lg_cv_results.loc[lg_cv_results[''] == 'test_f1', 'mean'].values[0] * 100, 2) Markdown(lg_cv_results.to_markdown(index = False)) ``` -Cross-validation results show strong performance with a test accuracy of 0.826 and train accuracy of 0.873. -The model's test precision (0.822), recall (0.8), and F1-score (0.809) demonstrate a balance between precision and recall, -with a slightly higher train performance. The standard deviations indicate minimal variability in performance across the folds. +Cross-validation results show strong performance with a test accuracy of `{python} lg_test_accuracy_cv`% +and train accuracy of `{python} lg_train_accuracy_cv`%. +The model's test precision (`{python} lg_test_precision_cv`%), recall (`{python} lg_test_recall_cv`%), and F1-score (`{python} lg_test_f1_cv`%) +demonstrate a balance between precision and recall, with a slightly higher train performance. +The standard deviations indicate minimal variability in performance across the folds. ![Confusion Matrix of Logistic Regression Model](../results/tables/logistic_regression/logistic_regression_confusion_matrix.png){#fig-conf-m-lg} -Confusion matrix indicates good performance with fewer false positives (17) compared to false negatives (19) for the positive class. +Confusion matrix indicates good performance with fewer false positives compared to false negatives for the positive class. ### Logistic Regression: Coefficients +```{python} +lg_coeff = pd.read_csv("../results/tables/logistic_regression/logreg_coefficients.csv") + +chest_pain_type_asymptomatic_coef = round(lg_coeff.loc[lg_coeff['Feature'] == 'onehotencoder__chest_pain_type_asymptomatic', 'Coefficient'].values[0], 2) +num_of_vessels_0_coef = round(lg_coeff.loc[lg_coeff['Feature'] == 'onehotencoder__num_of_vessels_0.0', 'Coefficient'].values[0], 2) +``` + ![Coefficients of Logistic Regression Model](../results/tables/logistic_regression/logreg_coefficients.png){#fig-coef-lg} The coefficients of the Logistic Regression model reflect the impact of each feature on the likelihood of a positive outcome, with positive coefficients indicating an increased likelihood and negative coefficients indicating a decreased likelihood. -For example, features like `chest_pain_type_asymptomatic` (1.18), `thalassemia_reversable defect` (0.97), and `num_of_vessels_2.0` (0.91) - are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of - a positive outcome. In contrast, features such as `num_of_vessels_0.0` (-1.26), `chest_pain_type_non-anginal pain` (-0.93), - and `thalassemia_normal` (-0.58) show a negative relationship, meaning they decrease the likelihood of heart disease. The coefficients for scaled features like `age`, `cholesterol`, and `resting_blood_pressure` (e.g., 0.34 for `cholesterol`) indicate their contribution to the prediction, with the magnitude of the coefficient reflecting the strength of their influence on the model's outcome. Features with larger absolute coefficient values, such as `num_of_vessels_0.0` and `chest_pain_type_asymptomatic`, have a more significant impact on the model’s prediction. +For example, features like `chest_pain_type_asymptomatic` (`{python} chest_pain_type_asymptomatic_coef`) +are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of +a positive outcome (presence of hear disease). In contrast, features such as `num_of_vessels_0.0`% (`{python} num_of_vessels_0_coef`) +show a negative relationship, meaning they decrease the likelihood of heart disease. ### Logistic Regression: Test Results @@ -89,12 +110,19 @@ For example, features like `chest_pain_type_asymptomatic` (1.18), `thalassemia_r #| lg_precision_class_0 = round(lg_model_results.loc[lg_model_results[''] == '0', 'precision'].values[0] * 100, 2) lg_recall_class_0 = round(lg_model_results.loc[lg_model_results[''] == '0', 'recall'].values[0] * 100, 2) +lg_f1_class_0 = round(lg_model_results.loc[lg_model_results[''] == '1', 'f1-score'].values[0] * 100, 2) + lg_precision_class_1 = round(lg_model_results.loc[lg_model_results[''] == '1', 'precision'].values[0] * 100, 2) lg_recall_class_1 = round(lg_model_results.loc[lg_model_results[''] == '1', 'recall'].values[0] * 100, 2) +lg_f1_class_1 = round(lg_model_results.loc[lg_model_results[''] == '1', 'f1-score'].values[0] * 100, 2) + +lg_accuracy_test = round(lg_model_results.loc[lg_model_results[''] == 'accuracy', 'precision'].values[0] * 100, 2) + Markdown(lg_model_results.to_markdown(index = False)) ``` -Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (0.844). -For precision and recall, class 0 achieves precision of 0.827 and recall of 0.896, while class 1 has precision of -0.868 and recall of 0.786, leading to an F1-score of 0.86 for class 0 and 0.825 for class 1. \ No newline at end of file +Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (`{python} lg_accuracy_test`%). +For precision and recall, class 0 achieves precision of `{python} lg_precision_class_0`% and recall of `{python} lg_recall_class_0`%, +while class 1 has precision of `{python} lg_precision_class_1`% and recall of `{python} lg_recall_class_1`%, +leading to an F1-score of `{python} lg_f1_class_0`% for class 0 and `{python} lg_f1_class_1`% for class 1. \ No newline at end of file diff --git a/report/content/_discussion.qmd b/report/content/_discussion.qmd index 63e795b..0de17ce 100644 --- a/report/content/_discussion.qmd +++ b/report/content/_discussion.qmd @@ -10,15 +10,17 @@ and demographic factors. #### Overall Accuracy of the Classification Models Both the logistic regression and decision tree models demonstrated strong predictive capabilities. -The decision tree model achieved an accuracy of 73.33%, with a precision of 72.22% and recall of 81.25% -for class 0 (absence of heart disease) and a precision of 75.0% and recall of 64.29% for class 1 (presence of heart disease). -However, the decision tree model exhibited signs of potential overfitting, as indicated by the 100% training accuracy versus -a test accuracy of 72.5%, suggesting that it might not generalize well to unseen data. - -In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of 81.11%. -It exhibited balanced precision (78.18% for class 0 and 85.71% for class 1) and recall (89.58% for class 0 and 71.43% for class 1), -with a test accuracy of 87.5%, and a training accuracy of 89.6%. This higher performance suggests that logistic regression provides -a more reliable model for predicting heart disease, with fewer issues related to overfitting and better generalization to new data. +The decision tree model achieved an accuracy of `{python} dt_accuracy_test`%, with a precision of `{python} dt_precision_class_0`% and recall of `{python} dt_recall_class_0`% +for class 0 (absence of heart disease) and a precision of `{python} dt_precision_class_1`% and recall of `{python} dt_recall_class_1`% for class 1 (presence of heart disease). +However, the decision tree model exhibited signs of potential overfitting, as indicated by the `{python} dt_train_accuracy_cv`% training accuracy versus +a test accuracy of `{python} dt_accuracy_test`%, suggesting that it might not generalize well to unseen data. + +In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of `{python} lg_accuracy_test`%. +It exhibited balanced precision (`{python} lg_precision_class_0`% for class 0 and `{python} lg_precision_class_1`% for class 1) +and recall (`{python} lg_recall_class_0`% for class 0 and `{python} lg_recall_class_1`% for class 1), +with a test accuracy of `{python} lg_accuracy_test`%, and a training accuracy of `{python} lg_train_accuracy_cv`%. +This higher performance suggests that logistic regression provides a more reliable model for predicting heart disease, +with fewer issues related to overfitting and better generalization to new data. #### Key Predictive Features for Heart Disease @@ -36,9 +38,9 @@ confirming their relevance in the prediction of heart disease. The ability to predict whether an individual might develop heart disease based on health indicators and demographic factors was evaluated through both models. Logistic regression demonstrated a strong capability to predict the likelihood of heart disease, -with particularly high recall for the absence of heart disease (89.58%), meaning the model was effective in identifying healthy +with particularly high recall for the absence of heart disease (`{python} lg_recall_class_1`%), meaning the model was effective in identifying healthy individuals. Conversely, the decision tree model showed a more balanced performance but struggled to consistently predict class 1 -(heart disease), as reflected by its lower recall (64.29%) for this group. +(heart disease), as reflected by its lower recall (`{python} dt_recall_class_1`%) for this group. Overall, the findings suggest that machine learning models, particularly logistic regression, hold promise for accurately predicting heart disease diagnoses. However, there is still room for improvement in refining these models to reduce misclassifications, diff --git a/report/heart_disease_predictor_report.html b/report/heart_disease_predictor_report.html index e174678..87151be 100644 --- a/report/heart_disease_predictor_report.html +++ b/report/heart_disease_predictor_report.html @@ -3058,15 +3058,10 @@ .bi-suitcase2::before { content: "\f902"; } .bi-vignette::before { content: "\f903"; } - + - + @@ -3110,7 +3105,7 @@

    Table of contents

  4. References
  5. -

    Other Formats

    +

    Other Formats

    @@ -3492,7 +3487,7 @@

    EDA

    - +
    Figure 3: Feature Densities by Diagnosis @@ -3526,13 +3521,13 @@

    Dec

    - - + + + - @@ -3581,7 +3576,7 @@

    Dec -

    Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with a test accuracy of 0.74, suggesting good generalization. The model exhibits a precision of 0.709, recall of 0.737, and an F1-score of 0.718 on the test set, with relatively low variability (standard deviations of 0.059, 0.069, and 0.079, respectively). These results indicate a reasonable trade-off between precision and recall.

    +

    Cross-validation results show a high train accuracy of 1, indicating perfect fit on the training data, with a test accuracy of 74.0%, suggesting good generalization. The model exhibits a precision of 70.9%, recall of 73.7%, and an F1-score of 71.8% on the test set, with relatively low variability. These results indicate a reasonable trade-off between precision and recall.

    @@ -3592,7 +3587,7 @@

    Dec

    -

    Confusion matrix reveals a higher number of false positives (29) compared to false negatives (25), indicating some misclassification of the negative class (0).

    +

    Confusion matrix reveals a higher number of false positives compared to false negatives, indicating some misclassification of the negative class (target = 0).

    Decision Tree: Test Results

    @@ -3639,7 +3634,7 @@

    Decision Tree: -

    Test results show balanced performance across classes, with precision for class 0 (0.727) and class 1 (0.771), and F1-scores of 0.777 and 0.701 for class 0 and 1, respectively. The model’s overall accuracy is 0.744.

    +

    Test results show balanced performance across classes, with precision for class 0 (72.73%) and class 1 (77.14%), and F1-scores of 77.67% and 70.13% for class 0 and 1, respectively. The model’s overall accuracy is 74.44%.

    Logistic Regression: Cross-Validation Results

    @@ -3662,12 +3657,12 @@

    - - + + - + @@ -3717,7 +3712,7 @@

    @@ -3728,32 +3723,32 @@

    Logistic Regression: Coefficients

    - +
    Figure 6: Coefficients of Logistic Regression Model
    -

    The coefficients of the Logistic Regression model reflect the impact of each feature on the likelihood of a positive outcome, with positive coefficients indicating an increased likelihood and negative coefficients indicating a decreased likelihood. For example, features like chest_pain_type_asymptomatic (1.18), thalassemia_reversable defect (0.97), and num_of_vessels_2.0 (0.91) are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of a positive outcome. In contrast, features such as num_of_vessels_0.0 (-1.26), chest_pain_type_non-anginal pain (-0.93), and thalassemia_normal (-0.58) show a negative relationship, meaning they decrease the likelihood of heart disease. The coefficients for scaled features like age, cholesterol, and resting_blood_pressure (e.g., 0.34 for cholesterol) indicate their contribution to the prediction, with the magnitude of the coefficient reflecting the strength of their influence on the model’s outcome. Features with larger absolute coefficient values, such as num_of_vessels_0.0 and chest_pain_type_asymptomatic, have a more significant impact on the model’s prediction.

    +

    The coefficients of the Logistic Regression model reflect the impact of each feature on the likelihood of a positive outcome, with positive coefficients indicating an increased likelihood and negative coefficients indicating a decreased likelihood. For example, features like chest_pain_type_asymptomatic (1.18) are positively associated with the likelihood of heart disease, meaning that higher values of these features increase the chances of a positive outcome (presence of hear disease). In contrast, features such as num_of_vessels_0.0% (-1.26) show a negative relationship, meaning they decrease the likelihood of heart disease.

    Logistic Regression: Test Results

    -
    -
    +
    +
    Table 6: Test results of Logistic Regression Model
    -
    +

    fit_time0.00100.0020.002
    score_time0.006 0.0020
    test_accuracyfit_time0.0020.0010.0070.003
    score_time0.0020.004 0
    @@ -3789,7 +3784,7 @@

    Logistic -

    Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (0.844). For precision and recall, class 0 achieves precision of 0.827 and recall of 0.896, while class 1 has precision of 0.868 and recall of 0.786, leading to an F1-score of 0.86 for class 0 and 0.825 for class 1.

    +

    Test results show that Logistic Regression outperforms the Decision Tree in terms of overall accuracy (84.44%). For precision and recall, class 0 achieves precision of 82.69% and recall of 89.58%, while class 1 has precision of 86.84% and recall of 78.57%, leading to an F1-score of 82.5% for class 0 and 82.5% for class 1.

    @@ -3801,8 +3796,8 @@

    Results

    This analysis aimed to evaluate the accuracy and interpretability of machine learning models in predicting heart disease. Specifically, we sought to determine the (1) overall accuracy of classification models, (2) identify key predictive features, and (3) assess the ability of these models to predict the likelihood of a patient developing heart disease based on health indicators and demographic factors.

    Overall Accuracy of the Classification Models

    -

    Both the logistic regression and decision tree models demonstrated strong predictive capabilities. The decision tree model achieved an accuracy of 73.33%, with a precision of 72.22% and recall of 81.25% for class 0 (absence of heart disease) and a precision of 75.0% and recall of 64.29% for class 1 (presence of heart disease). However, the decision tree model exhibited signs of potential overfitting, as indicated by the 100% training accuracy versus a test accuracy of 72.5%, suggesting that it might not generalize well to unseen data.

    -

    In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of 81.11%. It exhibited balanced precision (78.18% for class 0 and 85.71% for class 1) and recall (89.58% for class 0 and 71.43% for class 1), with a test accuracy of 87.5%, and a training accuracy of 89.6%. This higher performance suggests that logistic regression provides a more reliable model for predicting heart disease, with fewer issues related to overfitting and better generalization to new data.

    +

    Both the logistic regression and decision tree models demonstrated strong predictive capabilities. The decision tree model achieved an accuracy of 74.44%, with a precision of 72.73% and recall of 83.33% for class 0 (absence of heart disease) and a precision of 77.14% and recall of 64.29% for class 1 (presence of heart disease). However, the decision tree model exhibited signs of potential overfitting, as indicated by the 100.0% training accuracy versus a test accuracy of 74.44%, suggesting that it might not generalize well to unseen data.

    +

    In comparison, the logistic regression model outperformed the decision tree model, achieving an overall accuracy of 84.44%. It exhibited balanced precision (82.69% for class 0 and 86.84% for class 1) and recall (89.58% for class 0 and 78.57% for class 1), with a test accuracy of 84.44%, and a training accuracy of 87.3%. This higher performance suggests that logistic regression provides a more reliable model for predicting heart disease, with fewer issues related to overfitting and better generalization to new data.

    Key Predictive Features for Heart Disease

    @@ -3811,7 +3806,7 @@

    Predictive Capability for Patient Diagnosis

    -

    The ability to predict whether an individual might develop heart disease based on health indicators and demographic factors was evaluated through both models. Logistic regression demonstrated a strong capability to predict the likelihood of heart disease, with particularly high recall for the absence of heart disease (89.58%), meaning the model was effective in identifying healthy individuals. Conversely, the decision tree model showed a more balanced performance but struggled to consistently predict class 1 (heart disease), as reflected by its lower recall (64.29%) for this group.

    +

    The ability to predict whether an individual might develop heart disease based on health indicators and demographic factors was evaluated through both models. Logistic regression demonstrated a strong capability to predict the likelihood of heart disease, with particularly high recall for the absence of heart disease (78.57%), meaning the model was effective in identifying healthy individuals. Conversely, the decision tree model showed a more balanced performance but struggled to consistently predict class 1 (heart disease), as reflected by its lower recall (64.29%) for this group.

    Overall, the findings suggest that machine learning models, particularly logistic regression, hold promise for accurately predicting heart disease diagnoses. However, there is still room for improvement in refining these models to reduce misclassifications, especially in predicting the presence of heart disease.

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