Cardiovascular disease prediction model using Machine Learning Algorithm

Cardiovascular disease prediction model using Machine Learning Algorithm

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Bibliografske podrobnosti
Glavni avtor:	Arefin Mirdha, MD Shamsul
Drugi avtorji:	Mostakim, Moin
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2023
Teme:	Cardiovascular Disease Machine Learning Random Forest (RF) Logistic Regression Decision Tree Na¨ıve Bayes Linear Algorithm Support Vector Machine (SVM) K-Nearest Neighbor Neural networks (Computer science); Artificial intelligence Machine learning
Online dostop:	http://hdl.handle.net/10361/18023

Podobne knjige/članki

Effectiveness of data mining in predicting heart diseases
od: Afrin, Shahria, et al.
Izdano: (2018)

Early grade prediction using profile data
od: Iqbal, Sumaiya, et al.
Izdano: (2021)

What is relevant in a text document a machine learning based approach
od: Mahmud, Abdullah Al, et al.
Izdano: (2021)

A feature selection approach to determine obesity using machine learning method
od: Antor, Shahidul Alam, et al.
Izdano: (2021)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
od: Rahee, Arnob, et al.
Izdano: (2021)

Prediction of earthquakes: a step towards predicting the unpredictable
od: Chowdhury, Maheen, et al.
Izdano: (2023)

Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
od: Sarkar, Ripa, et al.
Izdano: (2024)

Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
od: Al Amin, Istiak, et al.
Izdano: (2022)

Flood prediction using machine learning models
od: Syeed, Miah Mohammad Asif, et al.
Izdano: (2022)

Diagnosis of cervical cancer using effective hybrid model in Bangladesh
od: Saad-Ul-Kabir, Syed, et al.
Izdano: (2021)

A hybrid rumor detection model derived from a comparative study of supervised approaches
od: Aothoi, Mehzabin Sadat, et al.
Izdano: (2023)

A machine learning approach to predict young voter enthusiasm based on non-political factors
od: Rahman, Md. Nowroz Junaed, et al.
Izdano: (2021)

A machine learning approach to detect depression and anxiety using supervised learning
od: Ullas, Md Tahmidur Rahman, et al.
Izdano: (2019)

Prediction of coronary heart diseases using supervised machine learning algorithms
od: Salam, Nazia Binte, et al.
Izdano: (2023)

Credit card fraud detection using machine learning techniques
od: Kabir, Tasmia, et al.
Izdano: (2021)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
od: Turjo, Aquib Abtahi, et al.
Izdano: (2021)

Enhancing crops production based on environmental status using machine learning techniques
od: Talukder, Shiyam, et al.
Izdano: (2021)

Data analysis on the Bangladesh share market using Machine-Learning
od: Sadman, Syed Hassan, et al.
Izdano: (2021)

Best feature selection and data visualization for breast cancer prediction
od: Hemel, Tanjim Ahmed, et al.
Izdano: (2020)

Performance analysis of machine learning algorithms for Malware classification
od: Bushra, Raisa Hasan, et al.
Izdano: (2023)

Modelling and forecasting energy demand of Bangladesh using AI based algorithms
od: Mahmud, Booshra Nazifa, et al.
Izdano: (2019)

Estimating flood susceptibility of Bangladesh in the future year using machine learning
od: Alim, Sakib Bin, et al.
Izdano: (2021)

Supervising vehicle using pattern recognition
od: Lushan, Mihodi Hasan, et al.
Izdano: (2018)

Prediction of bipolar disorder from mental episodes using machine learning approach
od: Tasmeem, Sumaiya, et al.
Izdano: (2023)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
od: Farabe, Abdullah Al, et al.
Izdano: (2021)

A counseling system to predict the study path for freshmen
od: Usha, Rowshni Tasneem, et al.
Izdano: (2019)

EMG controlled bionic robotic arm using artificial intelligence and machine learning
od: Rupom, Farhan Fuad, et al.
Izdano: (2021)

Parking data analysis
od: Ahamed, Kasfi, et al.
Izdano: (2023)

Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
od: Bhattacharjee, Ruposri, et al.
Izdano: (2024)

Machine learning-based approach on predicting online shopping addiction using EEG signals
od: Nawer, Nafisa, et al.
Izdano: (2023)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
od: Alam, Auninda, et al.
Izdano: (2021)

Machine learning for stress prediction
od: Salehin, Sherajus, et al.
Izdano: (2023)

Heart disease prediction using techniques of classification in machine learning
od: Afrose, Sadia, et al.
Izdano: (2021)

Heart disease prediction system
od: Amit, Tayab Al Azad, et al.
Izdano: (2022)

Fighting depression: psychological approaches among Bangladeshi university students
od: Sen, Sanjib Kumar, et al.
Izdano: (2022)

Amazon product reviews sentiment analysis using supervised learning algorithms
od: Hawladar, Mohibullah, et al.
Izdano: (2021)

Travel time prediction using machine learning and weather impact on traffic conditions
od: Deb, Bilash, et al.
Izdano: (2018)

Predicting brain age from EEG signals using machine learning and neural network
od: Pratanu, Abul Mushfique Muslah, et al.
Izdano: (2023)

Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module
od: Shrestha, Soptorsi Paul, et al.
Izdano: (2021)

A machine learning approach to predict movie box-office success
od: Quader, Nahid, et al.
Izdano: (2018)