Prediction of Breast Cancer Using Machine Learning Techniques

Iqbal, Tahir; Farooq, Asif; Sarwar, Nadeem; Ashraf, Mohsin; Irshad, Asma

doi:10.32350/bsr.0401.04

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…We conducted an extensive comparative analysis with recent research endeavors (Dhar et al, 2021). Similar to research conducted in Iqbal et al (2022)'s study.…”

Section: Comparative Analysismentioning

confidence: 99%

Pneumonia Detection Technique Empowered with Transfer Learning Approach

Baig,

Haq,

Irshad

et al. 2024

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Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep learning is extensively applied in the realm of medicine and disease classification. Early diagnosis of pneumonia is essential so it can be efficiently treated with the type of antibiotics. Bacterium and viruses are the population's first cause of pneumonia and death. Bacteria and viruses are part of mammalian pathogens and the most invasive type of bacteria or virus causing many diseases. Bacterial infection is among the most common types of disease in all age groups, but most bacterial infectious diseases are not the same. Our research will propose a transfer learning-based approach for pneumonia prediction utilizing a dataset comprising chest X-ray images. The dataset-based images will be grouped into two groups based on the parameters. Our proposed model displayed an average accuracy of 94.54% on the dataset. The proposed model (PDTLA) performed well compared with previous quantitative and qualitative research studies. Pneumonia detection transfer learning algorithm (PDTLA) is the name of the modified model.

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“…We conducted an extensive comparative analysis with recent research endeavors (Dhar et al, 2021). Similar to research conducted in Iqbal et al (2022)'s study.…”

Section: Comparative Analysismentioning

confidence: 99%

Pneumonia Detection Technique Empowered with Transfer Learning Approach

Baig,

Haq,

Irshad

et al. 2024

View full text Add to dashboard Cite

show abstract

“…Other classifers such as LR, naive Bayes (NB), and k-nearest neighbor (KNN) also demonstrated noteworthy performance. Te study [31] utilizes PCA, discriminant analysis (DA), and LR to extract features from the breast cancer dataset. While achieving notable accuracy with a hybrid feature extraction technique, discriminant logistic (DA-LR), the study failed to discuss the data complexity, such as data balancing and cleaning issues.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

Md. Ashafuddula,

Islam,

Islam

2023

Applied Computational Intelligence and Soft Computing

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Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.

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Prediction of Breast Cancer Using Machine Learning Techniques

Cited by 2 publications

References 35 publications

Pneumonia Detection Technique Empowered with Transfer Learning Approach

Pneumonia Detection Technique Empowered with Transfer Learning Approach

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

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