Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Buana Information Technology and Computer Sciences (BIT and CS)
jaddoa, ahmed sami
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Liver Disease Prediction Model Based on Oversampling Dataset with RFE Feature Selection using ANN and AdaBoost algorithms jaddoa, ahmed sami; J. Saba, Samah; A.Abd Al-Kareem, Elaf
Buana Information Technology and Computer Sciences (BIT and CS) Vol 4 No 2 (2023): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v4i2.5565

Abstract

Liver disease counts are one of the most prevalent diseases all over the world and they are becoming very common these days and can be dangerous. Liver diseases are increasing all over the world due to different factors such as excess alcohol consumption, drinking contaminated water, eating contaminated food, and exposure to polluted air. The liver is involved in many functions related to the human body and if not functioned properly can affect the other parts too. Predication of the disease at an earlier stage can help reduce the risk of severity. This paper implemented oversampling dataset, feature selecting attributes, and performance analysis for the improvement of the accuracy of classification of liver patients in 3 phases. In the first phase, the z-score normalization algorithm has been implemented to the original liver patient data-sets that has been collected from the UCI repository and then works on oversampling the balanced dataset. In the second phase, feature selection of attributes is more important by using RFE feature selection. In the third phase, classification algorithms are applied to the data-set. Finally, evaluation has been performed based upon the values of accuracy. Thus, outputs shown from proposed classification implementations indicate that ANN algorithm performs better than AdaBoost algorithm with the help of feature selection with a 92.77% accuracy
Early Breast Cancer Detection in Coimbra Dataset Using Supervised Machine Learning (XGBoost) Jaddoa, Ahmed Sami
Buana Information Technology and Computer Sciences (BIT and CS) Vol 5 No 2 (2024): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v5i2.6478

Abstract

Worldwide, breast cancer (BC) represents one of the serious health concerns for adult females. The early detection and accurate prediction of risks are vital for the provision of optimum care and enhancement of patient outcomes. In the past few years, promising large data merging and ensemble learning algorithms appeared for the purpose of classification and prediction of BC risk. In the area of medical applications, methods of machine learning (ML) are crucial. Early diagnosis is necessary for a more efficient carcinoma treatment. This study’s aim is to classify the carcinoma with the use of the 10 predictors that are found in Breast Cancer Coimbra dataset (BCCD). Presently, early diagnoses are necessary. The rates of cancer survival could be raised in the case where it is discovered early. Methods of machine learning offer effective way for data classifying and making early disease diagnoses. This study utilizes BCCD for the classification of BC cases utilizing XGBoost algorithm. Based on performance criteria, early detection of BC is the primary goal. The XGBoost classifier in this research achieved 98% precision, 98.32% accuracy, 99% f1-score, and 97% recall.
Co-Authors A.Abd Al-Kareem, Elaf J. Saba, Samah