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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS)
Raed Seetan
Slippery Rock University
Computer Science & IT Engineering Health Professions

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Plant disease prediction using classification algorithms Maria Morgan; Carla Blank; Raed Seetan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp257-264

Abstract

This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.
Modeling and analyzing predictive monthly survival in females diagnosed with gynecological cancers Timothy Samec; Raed Seetan
International Journal of Public Health Science (IJPHS) Vol 10, No 4: December 2021
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v10i4.20936

Abstract

Cancer ranks as a leading cause of death worldwide; an estimated 1.7 million new diagnoses were reported in 2021. Ovarian cancer, the most lethal of gynecological malignancies, has no effective screening with over 70% of patients being diagnosed in an advanced stage. The aim of this study was to determine the most statistically significant contributing factors through a multivariate regression into the severity of female gynecological cancers. Data from the surveillance, epidemiology, and end results program (SEER) cancer database were utilized in this study. Several attempted multivariate linear regressions were implemented with further reduced models; however, a linear model could not be properly fit to the data. Because of unmet assumptions, a nonparametric moving, local regression, locally estimated scatterplot smoothing (LOESS), was performed. After smoothing factors were included to reduced-models, residual information was minimized although few conclusions can be drawn from the resulting statistics. These issues were prevalent mainly because of the massive variability in the data and inherent lack of linearity. This can be a significant issue with clinical data that does not dive deeper into cancer-dependent factors including genetic expression and cell surface receptor overexpression. General patient demographic data and diagnostic information alone does not provide enough detail to make a definite conclusion or prediction on patient survivability. Increased attention to the acquisition of tumor tissue for genomic and proteomic analysis in addition to next-generation sequencing methods can lead to significant improvements in prognostic predictions.
Predicting cardiovascular disease using different blood pressure guidelines Christopher M. Bopp; William Briggs; Catherine Orlando; Raed Seetan
International Journal of Public Health Science (IJPHS) Vol 12, No 2: June 2023
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v12i2.22188

Abstract

The criteria used to categorize patients as either hypertensive or normotensive were changed in 2017 by the American Heart Association and the American College of Cardiology (AHA/ACC). The updated guidelines lowered the criteria by which individuals are classified as hypertensive; systolic blood pressure (SBP) cut-off from ≥140 mmHg to ≥130 mmHg and diastolic blood pressure from ≥90 mmHg to ≥80 mmHg. The purpose of this study was to investigate what effect these changes in diagnostic criteria had on the ability of supervised learning to predict cardiovascular disease. Three models were developed and tested. Two models using a binned hypertension measure based on either the AHA/ACC new released guidelines or the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) original guidelines. The third model used SBP as a continuous variable. Data from 68,657 patients was processed through decision tree algorithm to determine which model offered the best accuracy. For both female and male subjects, the model with SBP returned the best area under the receiver operator characteristic curve and overall better sensitivity and specificity values. Our results showed that changing the criteria by which individuals are classified as hypertensive or normotensive negatively impacted the ability of decision tree to predict cardiovascular disease in both females and males.
Co-Authors Carla Blank Catherine Orlando Christopher M. Bopp Maria Morgan Timothy Samec William Briggs