Prediction of diabetes induced complications using different machine learning algorithms

Prediction of diabetes induced complications using different machine learning algorithms

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Bibliografske podrobnosti
Main Authors:	Rahman, Tahsinur, Farzana, Sheikh Mastura, Khanom, Aniqa Zaida
Drugi avtorji:	Alam, Md. Ashraful
Format:	Thesis
Jezik:	English
Izdano:	BRAC University 2018
Teme:	PCA Diabetes complications missForest SVM Naïve bayes Logistic regression Computers > Intelligence (AI) & Semantics Computers > Machine Theory Mathematical theory of computation
Online dostop:	http://hdl.handle.net/10361/10945

Podobne knjige/članki

Predicting subject area interest of a student using naive bayes
od: Hossain, Shakib
Izdano: (2016)

Assistive Guideline of Categorization for Competitive Programming Problems
od: Dhrubo, Najmus Sakib, et al.
Izdano: (2021)

Analysis on the effects of wallstreetbets on the stock market
od: Hossain, Abir, et al.
Izdano: (2022)

Stock market prediction using ensemble learning
od: Rabbani, Rakin Bin, et al.
Izdano: (2021)

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

The applications of data mining and machine learning in Bangladesh for disease pattern analysis and prediction
od: Mahmud, Mahmudul Hasan
Izdano: (2021)

Complications in small animal surgery : diagnosis, management, prevention /
Izdano: (1996)

Diabetes mellitus : a handbook for professionals /
od: Mahtab, Hajera
Izdano: (2007)

Identification of childhood leukemia using deep learning
od: Tultul, Farana Naz
Izdano: (2018)

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

A review of prevalence, complications, risk factors, knowledge assessment, selfmanagement, consciousness and treatment of diabetes mellitus in Bangladesh
od: Chowdhury, Wasifuzzaman
Izdano: (2018)

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

Recognizing sentimental emotions in text by using Machine Learning
od: Bushra, Tabassum Khan, et al.
Izdano: (2023)

Analyzing data of social media to evaluate customer behavior of companies using sentiment analysis
od: Shourin, Bushra, et al.
Izdano: (2018)

Developing a machine learning based prognostic model and a supporting web-based application for predicting the possibility of early diabetes and diabetic kidney disease
od: Amin, M.M. Shahriar, et al.
Izdano: (2021)

A novel and superior approach to diabetic management: utilization of a fuzzy-logic based system for precision insulin dosing in type 2 diabetes patients
od: Orni, Proma Roy
Izdano: (2018)

Prevalence of epidemiological influence, risk factors of type-2 Diabetes Mellitus and analysis of hypertension as a complication among the relatively newly diagnosed patients from BIRDEM hospital
od: Chowdhury, Ishtiak Ahmed
Izdano: (2018)

Recognition of and response to neonatal intrapartum-related complications in home-birth settings in Bangladesh
od: Vanderende, Kristin E, et al.
Izdano: (2017)

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

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

Maternal nutrition intervention and maternal complications in 4 districts of Bangladesh: a nested cross-sectional study
od: Todd, Catherine S., et al.
Izdano: (2022)

Ethnic predisposition of diabetes mellitus in the patients with previous history of gestational diabetes mellitus: A review
od: Gupta, Rajat Das, et al.
Izdano: (2022)

Cervical Cancer Detection from Cervix Image Using Pap smear Imaging through CNN
od: Bhuiyan, Mazedul Haque, et al.
Izdano: (2021)

The EM algorithm and extensions /
od: McLachlan, Geoffrey J., 1946-
Izdano: (2008)

Movie recommendation using link prediction
od: Dhrubo, Md Alif-uz-zaman, et al.
Izdano: (2023)

Yoga posture recognition using the deep learning process
od: Islam, Abidul, et al.
Izdano: (2023)

An effective machine learning approach for sentiment analysis of restaurant reviews
od: Karim, Rabita
Izdano: (2018)

A review on classes of anti diabetic drugs and synthetic methods to access them: classical and modern approaches
od: Shahriyar, Md. Sakif
Izdano: (2024)

A review on diabetic neuropathy and development of sustained release tablet containing ‘Epalrestat’
od: Dewan, Marzan
Izdano: (2019)

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

A comprehensive look into the association of diabetes with COVID- 19
od: Antora, Sabrina Islam
Izdano: (2023)

Determination of synthetic drugs as adulterant in herbal anti-diabetic medicines by HPLC
od: Chowdhury, Anika Tasnim
Izdano: (2018)

Speech emotion detection using supervised, unsupervised and feature selection algorithms
od: Rifat, Abu Nuraiya Mahfuza Yesmin, et al.
Izdano: (2019)

A review on the effectiveness of oral insulin in type I and type II Diabetes mellitus
od: Nahar, Nafiun
Izdano: (2021)

Demystifying machine learning models for IOT attack detection with explainable AI
od: Muna, Rabeya Khatun, et al.
Izdano: (2021)

A review on drug repurposing for diabetes epidemic
od: Lamia, Sadia Raisa
Izdano: (2022)

Bacteriology of diabetic foot ulcers and their antibiotic resistance ability
od: Baral, Poulomi, et al.
Izdano: (2023)

Flight fare prediction using machine learning
od: Noyon, Md. Shaim Hosan, et al.
Izdano: (2024)

Use of stem cells for the Study and treatment of type 1 diabetes
od: Ahsan, Safat
Izdano: (2024)

Double trouble: prevalence and factors associated with tuberculosis and diabetes comorbidity in Bangladesh
od: Sarker, Malabika, et al.
Izdano: (2018)