Early Detection Of Heart Disease Research Articles

Explainable AI-driven machine learning for heart disease detection using ECG signal

The majority of countries globally find that cardiovascular diseases have the greatest death rate. Early detection of heart disease is necessary to prevent deaths from it. Big data for medical diagnostics has made it easier to construct sophisticated machine learning (ML) and deep learning (DL)-based models for the early automatic identification of heart disease. Using three common ECG datasets, i.e., the Physionet Challenge 2016, the PASCAL Challenge Competition, and the MIT-BIH - random forest (RF) and extreme gradient boosting (XGBoost), two tree explainer classifiers are proposed for the detection of heart disease in this study. During pre-processing variety of techniques like bandpass filtering, denoising, butterworth filtering and zero-phase filtering of the signal are carried out. After the signals are restored to the time domain form using IDWT, DWT and EWT are utilized to extract features. SHapley Additive exPlanations (SHAP) is used for the feature importance which identifies important features that have more impact on the prediction output of the models. It is demonstrated from the simulation results that EWT with XGB performs well in the three datasets considered. RF explainer algorithm with EWT feature performs best and results in an AUC of 95.36 % for the PASCAL Challenge Competition dataset. However, with AUC values of 97.44 % and 98.25 % for the Physionet Challenge 2016 and MIT-BIH datasets, respectively, the XGB explainer algorithm with EWT features performs better than (DWT+XGB), (DWT+RF), and (EWT+RF). When all three datasets are compared to the current models, overall the suggested model performs better and has a higher AUC.

Prediction of heart disease using a novel slap swarm optimized multi-objective random forest

Heart disease is a leading cause of death worldwide, becoming a major health concern for many people. Among the most important tasks and regular diagnostic research entails the identified cardiac issues, like heart diseases, valve conditions, etc. Early detection of heart disease may save many lives. The application of machine learning techniques in the medical sector has advanced significantly. A novel Slap Swarm Optimized Multi-Objective Random Forest (SSO-MORF) approach was presented in the proposed work for predicting cardiac disease. For this suggested investigation, heart disease information using individual customer identification at the University of California Irvine (UCI) was utilized, and data mining methods like categorization were applied. Min–max normalization and principal component analysis (PCA) were used for data preparation and feature extraction. As a result, a rather basic supervised machine learning technique may be used to predict heart disease quite accurately and with excellent potential value. According to this study, a classification based on the RF technique produced results with 99.45% accuracy, 98.3% recall, 98.6% precision, and a 98.9% f1 score. This finding demonstrates that our system is more efficient at predicting cardiac disease than other cutting-edge techniques.

Cardiovascular disease prediction and a visualization platform building through machine learning

In the increasing trend of population aging, medical security has become an important issue in social life that cannot be ignored. The elderly population generally faces the threat of a variety of cardiovascular diseases, which not only bring potential hazards to their physical health, but also pose more serious challenges to the medical system. Therefore, there is an urgent need to monitor the health indicators of the elderly and provide timely medical care. This article shows the corresponding machine learning model, which can provide relatively accurate predictions based on a given data set. By comparing and studying three Bayesian-optimized algorithms and baseline models (including Decision Tree, Support Vector Machine, Logical Regression, Random Forest, XGB, LGBM), a relatively better algorithm was selected. In this paper, it is believed that the model performance given by LGBM after Bayesian optimization is relatively good. It has a sound framework, high accuracy in predicting cardiovascular disease, and good performance when processing large-scale data, making it feasible for application in the field of medical security. At the same time, this research builds a visualization platform to afford assistance in the early detection of heart disease, is more friendly to non-professionals, and has made contributions in the fields of medical security and public health.

The Effectiveness of Early Detection of Heart Disease and Health Promotion on Attitude and Lifestyle Changes of Residents At Risk Of Heart Disease Post-Covid-19 Pandemic

During the Covid-19 pandemic, heart disease patients were at very high risk of experiencing severe complications if exposed to the virus. One important effort in preventing cardiovascular diseases is to identify the cardiovascular risk factors in each individual. There are several models for detecting risk, one of which is the Jakarta Cardiovascular Score. Health promotion is also an effort to manage modifiable risk factors for heart disease. Health promotion is expected to prevent risky behaviors that lead to an increase in heart disease incidence in the long term. This study aims to assess the effectiveness of early detection and health promotion in changing the attitudes and lifestyles of residents at risk of heart disease post-Covid-19 pandemic. The research method used in this study is a quasi-experimental design with one group of 30 respondents. The study was conducted in the working area of the Pasar Minggu District Health Center in Jakarta. The results showed that after the intervention, which included early detection and health promotion, there was a change in attitude by 0.8 points, from 88.13 (before intervention) to 88.95 (after intervention). The T-test results showed a p-value of 0.004, indicating a statistically significant difference between before and after the intervention. The respondents' lifestyle also changed by 5.5 points, from 66.03 (before intervention) to 71.62 (after intervention), with a p-value of 0.004, indicating a statistically significant difference between before and after the intervention.

Cardiac Disease Diagnosis Using K-Nearest Neighbor Algorithm: A Study on Heart Failure Clinical Records Dataset

This article introduces an approach to diagnose heart diseases utilizing the K-Nearest Neighbor algorithm and diverse correlation filters for selecting the most pertinent attributes. Results high- light that meticulous filter selection enhances survival predictions in patients with heart diseases. Employing K = 5 and correlation filter CF = 0.1, key attributes for classification were identified as anemia, high blood pressure, serum creatinine, and sex. Omitting the 'time' attribute led to information loss but was crucial to prevent biases and generalize predictions across various clinical scenarios. Utilizing these classification parameters, we designed an Android mobile application called “Heart Info System”, functioning as an artificial intelligence service. It employs the K-Nearest Neighbor algorithm with optimal parameters to evaluate the probability of survival in the progression of heart disease. The main activity of the application retrieves data from a Firebase database. While the study results show promise, the accuracy of the application may be influenced by inaccurate or incomplete input data. Nevertheless, this application has the potential to improve the early detection of heart diseases, paving the way for life-saving interventions.

A Robust Predictive Model for Early Detection of Heart Disease using Machine Learning

Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning- based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. While medication is the primary treatment, exercise is increasingly recognized as an effective adjunct therapy in managing heart failure. This research project aims to develop a robust predictive model for the early detection of heart diseases by leveraging machine learning techniques, with a specific focus on the application of Support Vector Machines (SVM). The growth in technology has improved the information or data which can be extracted from a patient to help pinpoint the cause of illness. Using different number of attributes or data from the medical profile of a patient can predict the chance of a patient developing a heart condition. In simple terms, these attributes are loaded into logistic regression, Decision Tree, Random Forest, SVM, KNN and Naive bayes, that is, Machine learning (ML) algorithms for the analysis and further prediction of heart disease. There are many other techniques, methods used by other researchers. By using this method, the standards in the medical industries are elevated and rose as they can provide better diagnostics and treatment of the patient, resulting in providing an overall good quality service. This has its main focus towards: Using Data analysis, creating prediction Models to provide early detection of Heart Diseases, Also, by creating a reliable method to predict heart disease. Thus, we found that supervised machine learning algorithms can be used to make heart disease predictions with very high accuracy and excellent potential utility. Our proposed research study has significant scientific contributions to the medical community. Key Words: Machine learning, heart failure, cross validations, feature engineering, Naive Bayes, k Nearest Neighbor (KNN), Decision tree, Artificial Neural Network (ANN), Random Forest, Heart Disease.

Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts The Heart Disease Converging Nature-Motivated Optimization and Machine Learning

Background: As one of the leading causes of morbidity and mortality worldwide, the diagnosis of acute cardiovascular disease (acute CVD) in emergency settings remains a significant challenge. Machine learning (ML) is artificial intelligence that allows computer programs to learn from and analyze large data sets without human intervention. As a diagnostic tool, ML presents numerous advantages, such as convenience, speed, and affordability. Methods: This research utilized a new model for predicting heart disease for feature selection(Wild Horse Optimizer, WHO) and another classification model (Support Vector Machine, SVM). In the WHO algorithm, like envisioning social behaviors of wild horses, we observe that wild horses display diverse behaviors like leading, grazing, moving, mating and chase. One odd behavior about wild horses is that young foals will break away from the herd to avoid the breed in its related herd before reaching maturity. It defines the decencies of the horses. Discussion: Multiple ML techniques were employed to train classifiers using the Cleveland heart disease dataset after conducting feature selection using WHO algorithm. The performance of these models was evaluated based on different parameters like Sensitivity, Accuracy, Specificity, and AUC. The SVM classifier model trained using the WHO approach proved better than the existing methods. In the research, we proposed a Wild Horse Optimization algorithm to optimize the features for Heart Disease prediction. After feature optimization, the optimized dataset is then given to Support Vector machine classification, which performs the classification of heart disease. After evaluating different experimental results, authors conclude that combining this feature selection algorithm and SVM as a classifier for forecasting heart disease is very effective. With the help of this approach, we can improve the early detection of heart disease and effectively manage severe heart disease. The experimental result graph shows an increased accuracy rate of the spectra feature subject.

Smart Artificial Intelligence System for Heart Disease Prediction

Heart disease playing a vital role in human life, Early detection of heart-disease we can save humans lives and it remains a leading cause of mortality worldwide, making early and accurate prediction of heart disease a critical task for improving patient outcomes. Machine learning has shown great promise in this area, with various models being developed to predict heart disease based on a range of clinical and demographic features. However, there is a growing need for more efficient machine learning models that can accurately predict heart disease while minimizing computational costs, particularly in resource-constrained settings. This research paper proposes an efficient machine learning model for heart disease prediction that combines feature selection, model optimization, and interpretability techniques to achieve accurate predictions with reduced computational complexity. The proposed model utilizes a dataset of clinical and demographic features, such as age, sex, blood pressure, cholesterol levels, and other relevant risk factors, to train a machine learning model using a large real-world dataset. The proposed efficient machine learning model is evaluated on benchmark datasets and compared with other state-of-the-art models in terms of precision, Accuracy, Recall and F1- Score. The results demonstrate the model achieved by superior prediction performance to existing models. Proposed method accuracy increased by 4.8%

A proposed model using Naïve Bayes and generalized linear models for early detection of heart attack risk

<span>Timely identification of diseases, particularly heart attacks is crucial for individuals, particularly the elderly, to accurately anticipate the onset of the disease based on symtoms. The objective of this study is to develop a highly accurate model for early detection of heart disease using the Naïve Bayes (<span>NB) and generalized linear model (GLM) techniques. In addition, another concern is the model’s subfar accuracy levels, promting the implementation of measures to optimize it. The suggested approach fot optimization involves</span> <span>the utilization of a genetic algorithm (GA). The research findings indicate that the NB and GLM approaches achive a reasonably high level of accuracy. Specifically, the NB model achieves an</span> <span>accuracy of 82.53%, while the GLM achieves an accuracy of 84.50%. Following optimization, the accuracy levels notably improved, with the NB_M-GA model reaching 85.83% and the GLM_M-GA model achieving 86,48%.</span></span>

PENGELOMPOKAN TINGKAT RESIKO PENYAKIT JANTUNG BERDASARKAN USIA MENGGUNAKAN ALGORITMA K-MEANS

Heart disease is one of the non-communicable diseases that can cause death, This disease occurs due to a narrowing of the blood vessels so as to cause impaired heart function Some of the causes of heart disease are one of them based on age, basically heart disease can be prevented by various factors including a healthy lifestyle, besides that early detection of heart disease is also needed to prevent death in sufferers One way to do early detection is to use data mining. The use of the k-means algorithm can be done to cluster the grouping of heart diseases by age to find out someone is exposed to the cause of high and low heart disease. Based on these problems, this study uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) method with several stages such as Business Understanding, Data Understanding, Data preparation, Modeling, Evaluation, and Deployment. The clustering method with the k-means algorithm in this study shows a new insight, namely grouping the risk level of heart disease based on 3 clusters. Cluster 0 is an age category with a fairly low risk level of heart disease or Low, which is 355 out of 1025 age categories tested, then cluster 1 is an age category with a moderate or Medium heart disease risk level, which is 208 out of 1025 age categories tested, and finally cluster 2 is an age category with a fairly high age category or High, which is 462 out of 1025 age categories tested.

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.

Comparison PSO And IWPSO Performance In Optimizing Decision Tree Algorithm On Heart Disease Dataset

Heart disease, one of the most common and potentially fatal chronic diseases, has become a major focus in global health efforts. In this study, researchers used the decision tree algorithm on the heart disease dataset with the stages of the decision algorithm including the EDA, Split Data, and Decision tree modeling stages. Furthermore, hyperparameters use PSO and IWPSO to optimize the algorithm. The purpose of this research is to analyze the performance of Particle Swarm Optimization (PSO) and Inertia Weight Particle Swarm Optimization (IWPSO) in heart disease prediction based on relevant datasets. PSO and IWPSO were applied to the heart disease dataset, with the results showing an accuracy rate of 78% for PSO and 84% for IWPSO. These results indicate that IWPSO provides significant performance improvement compared to PSO in the context of heart disease prediction. The implications of these findings can support the development of more efficient prediction systems for early detection of heart disease, making a positive contribution to prevention efforts and further treatment of this critical health condition. In addition, the purpose of this research is to continue research in the form of C4.5 on heart disease with a result of 80.43%. In this study, IWPSO got the best accuracy of 84.23% greater than previous research. The results of this study are to provide insight that PSO and IWPSO hyperparameters can optimize decision trees in handling heart disease datasets and continue research.

Comparison of Hyperparameter Optimization Techniques in Hybrid CNN-LSTM Model for Heart Disease Classification

Heart disease, which causes the highest number of deaths worldwide, recorded about 17.9 million cases in 2019, or about 32% of total global deaths, according to the World Health Organization (WHO). The significance of early detection of heart disease drives research to develop effective diagnosis systems utilizing machine learning. The advancement of machine learning in healthcare currently primarily serves as a supporting role in the ability of clinicians or analysts to fulfill their roles, identify healthcare trends, and develop disease prediction models. Meanwhile, deep learning has experienced rapid development and has become the most popular method in recent years, one of which is detecting diseases. The main objective of this research is to optimize the hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model for classifying heart disease by comparing hyperparameter optimization using grid search and random search. Although random search requires less time in hyperparameter tuning, the classification performance results of grid search show higher accuracy. In the test, the hybrid CNN and LSTM model with grid search achieved 91.67% accuracy, 89.66% recall (sensitivity), 93.55% specificity, 92.86% precision, 91.23% f1-score, and 0.9310 AUC value. These results confirm that using a hybrid CNN and LSTM model with a grid search approach is better suited for classifying heart disease.

Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults

The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-specific strain values in healthy children and young adults using a deformable registration algorithm (DRA). A retrospective study included 131 healthy children and young adults (62 males and 69 females) with a mean age of 16.6±3.9 years. CMR examinations were conducted using 1.5T scanners, and strain analysis was performed using TrufiStrain research prototype software (Siemens Healthineers, Erlangen, Germany). Global and layer-specific strain parameters were extracted from balanced Steady-state free precession cine images. Statistical analyses were conducted to evaluate the impact of demographic variables on strain measurements. The peak global longitudinal strain (LS) was -16.0±3.0%, peak global radial strain (RS) was 29.9±6.3%, and peak global circumferential strain (CS) was -17.0±1.8%. Global LS differed significantly between males and females. Transmural strain analysis showed a consistent pattern of decreasing LS and CS from endocardium to epicardium, while radial strain increased. Basal-to-apical strain distribution exhibited decreasing LS and increasing CS in both global and layer-specific analysis. This study uses DRA to provide reference values for global and layer-specific strain in healthy children and young adults. The study highlights the impact of sex and age on LS and body mass index on RS. These insights are vital for future cardiac assessments in children, particularly for early detection of heart diseases.

ECG Based Heart Disease Classification: Advancement and Review of Techniques

The heart plays a crucial role in pumping blood to our organs. However, without early detection of heart disease (HD), the outcomes can be fatal. That’s why it is crucial to emphasize the significance of prior detection and accurate classification of HD for effective diagnosis. One important diagnostic technique that has emerged is electrocardiography (ECG). Analyzing and processing ECG signals greatly contribute to diagnosing HD. In this comprehensive review, we discuss recent advancements in HD classification using various machine learning, deep learning, and ensemble learning algorithms such as SVM, CNN, XGBoost, etc. We delve into the advantages and disadvantages of each algorithm while providing an extensive overview of state-of-the-art studies that have utilized these algorithms on datasets MIT-BIH and PTB-XL. Among these models, CNN consistently achieved remarkable accuracy across both datasets with an average of 98.83%. The LSTM model excelled in sensitivity on the PTB-XL dataset with an average recall rate of 88.01%. Additionally, SVM and k-NN models delivered competitive accuracy rates of 96.20% and 97.50%, respectively, on the MIT-BIH dataset. We also explore GMM and K-Means clustering techniques to provide insights into their potential applications in classifying HD. It’s worth noting that existing research often focuses on individual methods without comprehensive comparisons between available algorithms. Our paper aims to bridge this gap by offering a concise overview that encompasses various methods’ strengths, limitations, and advancements, providing valuable insights for future studies.

Hidden Markov Models for early detection of cardiovascular diseases

Introduction: This article, developed between 2022 and 2023 within the framework of Applied Stochastic Processes by the SciBas group at the Universidad Distrital Francisco José de Caldas, focuses on the role of Hidden Markov Models (HMM) in predicting cardiovascular diseases. Problem: The addressed issue is the need to enhance the early detection of heart diseases, emphasizing how HMM can address uncertainty in clinical data and detect complex patterns. Objective: To evaluate the use of Hidden Markov Models (HMM) in the analysis of electrocardiograms (ECG) for the early detection of cardiovascular diseases. Methodology: The methodology comprises a literature review concerning the relationship between HMM and cardiovascular diseases, followed by the application of HMM to prevent heart attacks and address uncertainty in clinical data. Results: The findings indicate that HMM is effective in preventing heart diseases, yet its effectiveness is contingent upon data quality. These results are promising but not universally applicable. Conclusions: In summary, this study underscores the utility of HMM in early infarction detection and its statistical approach in medicine. It is emphasized that HMM is not infallible and should be complemented with other clinical options and assessment methods in real-world situations. Originality: This work stands out for its statistical and probabilistic approach in the application of Hidden Markov Models (HMM) in medical analysis, offering an innovative perspective and enhancing the understanding of their utility in the field of medicine. Limitations: It is recognized that there are limitations, such as dependence on data quality and variable applicability in clinical cases. These limitations should be considered in the context of their implementation in medical practice.

Multiclass Classification of Myocardial Infarction Based on Phonocardiogram Signals Using Ensemble Learning

Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.

An Intelligent Heart Disease Prediction Framework Using Machine Learning and Deep Learning Techniques

Cardiovascular diseases (CVD) rank among the leading global causes of mortality. Early detection and diagnosis are paramount in minimizing their impact. The application of ML and DL in classifying the occurrence of cardiovascular diseases holds significant potential for reducing diagnostic errors. This research endeavors to construct a model capable of accurately predicting cardiovascular diseases, thereby mitigating the fatality associated with CVD. In this paper, the authors introduce a novel approach that combines an artificial intelligence network (AIN)-based feature selection (FS) technique with cutting-edge DL and ML classifiers for the early detection of heart diseases based on patient medical histories. The proposed model is rigorously evaluated using two real-world datasets sourced from the University of California. The authors conduct extensive data preprocessing and analysis, and the findings from this study demonstrate that the proposed methodology surpasses the performance of existing state-of-the-art methods, achieving an exceptional accuracy rate of 99.99%.

Heart Disease Detection by Machine Learning System

Heart disease is a prevalent global health issue that impacts a substantial number of individuals worldwide. It is characterized by symptoms such as shortness of breath, muscle weakness, and swollen feet. However, the current diagnostic methods for heart disease have limitations in terms of accuracy and efficiency, making early detection challenging. Consequently, researchers are striving to develop an effective approach for early detection of heart disease. The lack of advanced medical equipment and qualified healthcare professionals further complicates the diagnosis and management of cardiac conditions., there have been approximately 26 million reported cases of heart disease, with an additional 3.6 million new cases identified annually. In the United States, a significant proportion of the population is affected by heart disease. Typically, doctors diagnose heart disease by considering the patient's medical history, conducting a physical examination, and assessing any concerning symptoms. However, this diagnostic method does not consistently provide accurate identification of individuals with heart disease. The importance of employing. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. “Patient 1,patient 2,patient 3 and patient 4.” Dyspnea can be described as a sensation of breathlessness and inadequate breathing, where one feels unable to take in enough air or breathe deeply. It involves the interplay of mechanoreceptors in the upper airways, lungs, and chest wall, along with peripheral receptors, chemoreceptors, and other sensory receptors. Edema refers to the accumulation of excessive fluid in the body tissues, leading to swelling. While edema can occur in any part of the body, it is more commonly observed in the lower extremities Ascites - The pathological buildup of fluid in the abdominal cavity is known as ascites. It is the most frequent cirrhosis consequence and happens in 50% of patients with decompensated cirrhosis within 10 years. Ascites formation marks the change from stressed to decompensated cirrhosis. Patent 1 is in rank 1 and patient 5 is ranked 5. In weighted table every value is equally split by 1,so that each value is equal. In the study, the researchers compared the sensitivity levels of two classifiers: the Relief FS method with a linear SVM classifier and the NB classifier with specific features from the LASSO FS algorithm. The findings revealed that the NB classifier, utilizing LASSO FS features, exhibited the highest performance in terms of sensitivity. Additionally, the Logistic Regression MCC classifier, employing the FCMIM FS method, achieved a classification accuracy of 91%.

Heart disease Prediction and detection using Machine Learning Algorithms

This paper focuses on building a heart disease detection system using machine learning. A Python- based application is developed for healthcare research, offering reliability and versatility. The process involves handling categorical data, collecting databases, KNN, and attribute evaluation. A KNN is introduced for improved accuracy, around 96.58%, in identifying heart diseases. The algorithm's experiments and outcomes are discussed, highlighting enhanced research diagnosis accuracies. The paper concludes by summarizing objectives, limitations, and research contributions . Cardiovascular diseases (CVDs) are a leading cause of global morbidity and mortality. Early detection of heart diseases is crucial for effective intervention and prevention. The existing diagnostic methods often involve complex and expensive procedures. Therefore, there is a pressing need to develop a robust and accessible Heart Disease Detection System using Machine Learning (ML) algorithms