Heart attack prediction using machine learning and XAI
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.
| Главный автор: | |
|---|---|
| Другие авторы: | |
| Формат: | Диссертация |
| Язык: | English |
| Опубликовано: |
Brac University
2023
|
| Предметы: | |
| Online-ссылка: | http://hdl.handle.net/10361/18012 |
| id |
10361-18012 |
|---|---|
| record_format |
dspace |
| spelling |
10361-180122023-10-25T03:14:15Z Heart attack prediction using machine learning and XAI Ahsan, Mumtahina Rabiul Alam, Md. Golam Department of Computer Science and Engineering, Brac University Heart Attack ML (Machine Learning) XAI (Explainable Artificial Intelligence) SHAP (SHapley Additive exPlanations) Shapley Value LIME (Local Interpretable Model-Agnostic Explanations) Black-Box XGBoost KNN Neural networks (Computer science) Artificial intelligence Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-32). Predictive analytics has received a lot of attention in recent years due to advances in supporting technology, particularly in the areas of big data and machine learning. In recent years, the uses of disease prediction has been seen in the healthcare area. Among so many predictions, this project will show the prediction of a heart attack. Heart disease, often known as cardiovascular disease, refers to a variety of illnesses that affect the heart and has become the leading cause of mortality worldwide in re cent decades. It links a slew of risk factors for heart disease with a pressing need for precise, dependable, and practical methods for making an early diagnosis and man aging the condition. In the healthcare industry, data mining is a typical methodology for analyzing large amounts of data. Because predicting cardiac illness is a difficult undertaking. It is necessary to automate the process in order to avoid the risks connected with it and to inform the patient well in advance. Heart diseases can be determined using data mining techniques such as XGBOOST, Logistic Regression, Stochastic Gradient Descent, Support Vector Classifier, Kneighborsclassifier, and Naive Bayes. With this project, I have shown that among all the above machine learning models, XGBOOST outperforms other techniques in terms of predicting heart attacks. As a result, this paper conducts a comparative study of the perfor mance of several machine learning algorithms. For any type of prediction features of the dataset plays a very important role. Features can give positive or negative impact on the final prediction. The features importance can be visualized by the XAI methods. This paper also takes an approach to interpret the explainability of the model’s prediction. By using the XAI method SHAP and LIME with the help of the concept of black box, this research conducts the KNN algorithms prediction. Mumtahina Ahsan M. Computer Science and Engineering 2023-03-27T06:39:42Z 2023-03-27T06:39:42Z 2022 2022-09 Thesis ID: 20266025 http://hdl.handle.net/10361/18012 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 32 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Heart Attack ML (Machine Learning) XAI (Explainable Artificial Intelligence) SHAP (SHapley Additive exPlanations) Shapley Value LIME (Local Interpretable Model-Agnostic Explanations) Black-Box XGBoost KNN Neural networks (Computer science) Artificial intelligence Machine learning |
| spellingShingle |
Heart Attack ML (Machine Learning) XAI (Explainable Artificial Intelligence) SHAP (SHapley Additive exPlanations) Shapley Value LIME (Local Interpretable Model-Agnostic Explanations) Black-Box XGBoost KNN Neural networks (Computer science) Artificial intelligence Machine learning Ahsan, Mumtahina Heart attack prediction using machine learning and XAI |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022. |
| author2 |
Rabiul Alam, Md. Golam |
| author_facet |
Rabiul Alam, Md. Golam Ahsan, Mumtahina |
| format |
Thesis |
| author |
Ahsan, Mumtahina |
| author_sort |
Ahsan, Mumtahina |
| title |
Heart attack prediction using machine learning and XAI |
| title_short |
Heart attack prediction using machine learning and XAI |
| title_full |
Heart attack prediction using machine learning and XAI |
| title_fullStr |
Heart attack prediction using machine learning and XAI |
| title_full_unstemmed |
Heart attack prediction using machine learning and XAI |
| title_sort |
heart attack prediction using machine learning and xai |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18012 |
| work_keys_str_mv |
AT ahsanmumtahina heartattackpredictionusingmachinelearningandxai |
| _version_ |
1814308977519886336 |