# Effective Heart Disease Prediction Using Machine Learning Techniques
## 5 Conclusions  
The primary objective of this study was to classify heart disease using different models and a real-world dataset. The k-modes clustering algorithm was applied to a dataset of patients with heart disease to predict the presence of the disease. The dataset was preprocessed by converting the age attribute to years and dividing it into bins of 5-year intervals, as well as dividing the diastolic and systolic blood pressure data into bins of 10 intervals. The dataset was also split on the basis of gender to take into account the unique characteristics and progression of heart disease in men and women.  
The elbow curve method was utilized to determine the optimal number of clusters for both the male and female datasets. The results indicated that the MLP model had the highest accuracy of 87.23%. These findings demonstrate the potential of k-modes clustering to accurately predict heart disease and suggest that the algorithm could be a valuable tool in the development of targeted diagnostic and treatment strategies for the disease. The study utilized the Kaggle cardiovascular disease dataset with 70,000 instances, and all algorithms were implemented on Google Colab. The accuracies of all algorithms were above 86% with the lowest accuracy of 86.37% given by decision trees and the highest accuracy given by multilayer perceptron, as previously mentioned.  
Limitations. Despite the promising results, there are several limitations that should be noted. First, the study was based on a single dataset and may not be generalizable to other populations or patient groups. Furthermore, the study only considered a limited set of demographic and clinical variables and did not take into account other potential risk factors for heart disease, such as lifestyle factors or genetic predispositions. Additionally, the performance of the model on a held-out test dataset was not evaluated, which would  
Figure 6: ROC–area under curve of **(a)** MLP, **(b)** RF, **(c)** DT, and **(d)** XGB.  
have provided insight on how well the model generalizes to new, unseen data. Lastly, the interpretability of the results and the ability to explain the clusters formed by the algorithm was not evaluated. In light of these limitations, it is recommended to conduct further research to address these issues and to better understand the potential of k-modes clustering for heart disease prediction.  
Future research. Future research could focus on addressing the limitations of this study by comparing the performance of the k-modes clustering algorithm with other commonly used clustering algorithms, such as k-means [35] or hierarchical clustering [36], to gain a more comprehensive understanding of its performance. Additionally, it would be valuable to evaluate the impact of missing data and outliers on the accuracy of the model and develop strategies for handling these cases. Furthermore, it would be beneficial to evaluate the performance of the model on a held-out test dataset in order to establish its generalizability to new, unseen data. Ultimately, future research should aim to establish the robustness and generalizability of the results and the interpretability of the clusters formed by the algorithm, which could aid in understanding the results and support decision making based on the study's findings.  
P.P., conceptualization, data curation, methodology, software, validation, visualization, writing--original draft; C.M.B., conceptualization, supervision, project administration, writing--review and editing; T.G., conceptualization, data curation, methodology, software, validation, visualization, writing--original draft; P.L.M., funding acquisition, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors declare no conflict of interest.