Impact of Outliers and Dimensionality Reduction on the Performance of Predictive Models for Medical Disease Diagnosis

Ozsahin, Dilber Uzun; Mustapha, Mubarak Taiwo; Mubarak, Auwalu Saleh; Ameen, Zubaida Said; Uzun, Berna

doi:10.1109/aie57029.2022.00023

Cited by 10 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During this process, unnecessary columns were eliminated, and missing values were added [ 20 , 21 ]. The next step was to arrange the dataset according to the order that would enable evaluation.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

View full text Add to dashboard Cite

The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models—specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)—were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

show abstract

Section: Methodsmentioning

confidence: 99%

“… For our prediction, only datasets from Israel were used to train the model. Data normalization [ 21 , 22 ] was carried out prior to modeling using Equation (1).

where

is the measured data

and

are the minimum and maximum values, respectively.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Ozsahin

Onakpojeruo²,

Duwa³

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Data preprocessing is a crucial and common first step in any deep learning project [ 21 , 22 ]. It enables raw data to be adequately prepared in formats acceptable by the network.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears

Ozsahin

Mustapha²,

Duwa³

et al. 2022

Diagnostics

Self Cite

View full text Add to dashboard Cite

Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.

show abstract

“…Ensemble methods combine numerous models to enhance prediction accuracy [22]. Dimensionality reduction constitutes another potential method to enhance the predictive accuracy of ML models by reducing unimportant inputs, for instance by reducing image pixels (which can be thought of as inputs or ''dimensions'') that carry no diagnostic value on CT or MRI scans [23]. Unsupervised learning involves feeding an algorithm with unlabeled data to identify patterns and relationships within the data without any external input [24].…”

Section: Machine Learning In Healthcarementioning

confidence: 99%

Machine learning applications and challenges in graft-versus-host disease: a scoping review

Mushtaq,

Shafqat,

Salah

et al. 2023

Current Opinion in Oncology

View full text Add to dashboard Cite

Purpose of review This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. Recent findings Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, “snapshot” assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. Summary To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.

show abstract

Impact of Outliers and Dimensionality Reduction on the Performance of Predictive Models for Medical Disease Diagnosis

Cited by 10 publications

References 26 publications

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach

Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears

Machine learning applications and challenges in graft-versus-host disease: a scoping review

Contact Info

Product

Resources

About