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Uzun Ozsahin D, Duwa BB, Ozsahin I, Uzun B. Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest. Diagnostics (Basel) 2024; 14:385. [PMID: 38396424 PMCID: PMC10888406 DOI: 10.3390/diagnostics14040385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models-such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier-is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.
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Affiliation(s)
- Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
| | - Basil Barth Duwa
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
| | - Ilker Ozsahin
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Berna Uzun
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey; (B.B.D.); (I.O.)
- Department of Mathematics, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
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Umar H, Rizaner N, Usman AG, Aliyu MR, Adun H, Ghali UM, Uzun Ozsahin D, Abba SI. Prediction of Cell Migration in MDA-MB 231 and MCF-7 Human Breast Cancer Cells Treated with Albizia Lebbeck Methanolic Extract Using Multilinear Regression and Artificial Intelligence-Based Models. Pharmaceuticals (Basel) 2023; 16:858. [PMID: 37375805 DOI: 10.3390/ph16060858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/28/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. Albizia lebbeck is a valuable plant with medicinal properties due to some active biological macromolecules, and it's cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of A. lebbeck methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 μg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 μg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (p < 0.05; n ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (p < 0.05; n ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis.
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Affiliation(s)
- Huzaifa Umar
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
| | - Nahit Rizaner
- Biotechnology Research Centre, Cyprus International University, TRNC Mersin 10, Nicosia 99258, Turkey
| | - Abdullahi Garba Usman
- Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia 99138, Turkey
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, Nicosia 99138, Turkey
| | - Maryam Rabiu Aliyu
- Department of Energy System Engineering, Cyprus International University, TRNC, Mersin 10, Nicosia 99258, Turkey
| | - Humphrey Adun
- Department of Energy System Engineering, Cyprus International University, TRNC, Mersin 10, Nicosia 99258, Turkey
| | - Umar Muhammad Ghali
- Department of Medical Biochemistry, Faculty of Medicine, Near East University, TRNC, Mersin 10, Nicosia 99138, Turkey
| | - Dilber Uzun Ozsahin
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
| | - Sani Isah Abba
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
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Madaki Z, Abacioglu N, Usman AG, Taner N, Sehirli AO, Abba SI. Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010079. [PMID: 36676028 PMCID: PMC9866913 DOI: 10.3390/life13010079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022]
Abstract
The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.
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Affiliation(s)
- Zachariah Madaki
- Department of Pharmacology, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - Nurettin Abacioglu
- Department of Pharmacology, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - A. G. Usman
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
- Correspondence: (A.G.U.); (S.I.A.)
| | - Neda Taner
- Department of Clinical Pharmacy, Faculty of Pharmacy, Istanbul Medipol University, 34810 Istanbul, Türkiye
| | - Ahmet. O. Sehirli
- Department of Pharmacology, Faculty of Dentistry, Nicosia, Near East University, North Cyprus, Mersin-10, 99138 Nicosia, Türkiye
| | - S. I. Abba
- Interdisciplinary Research Centre for Membrane and Water Security, Faculty of Petroleum and Minerals, King Fahd University, Dhahran 31261, Saudi Arabia
- Correspondence: (A.G.U.); (S.I.A.)
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Clinical Modelling of RVHF Using Pre-Operative Variables: A Direct and Inverse Feature Extraction Technique. Diagnostics (Basel) 2022; 12:diagnostics12123061. [PMID: 36553067 PMCID: PMC9777038 DOI: 10.3390/diagnostics12123061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/23/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Right ventricular heart failure (RVHF) mostly occurs due to the failure of the left-side of the heart. RVHF is a serious disease that leads to swelling of the abdomen, ankles, liver, kidneys, and gastrointestinal (GI) tract. A total of 506 heart-failure subjects from the Faculty of Medicine, Cardiovascular Surgery Department, Ege University, Turkey, who suffered from a severe heart failure and are currently receiving support from a ventricular assistance device, were involved in the current study. Therefore, the current study explored the application of both the direct and inverse modelling approaches, based on the correlation analysis feature extraction performance of various pre-operative variables of the subjects, for the prediction of RVHF. The study equally employs both single and hybrid paradigms for the prediction of RVHF using different pre-operative variables. The visualized and quantitative performance of the direct and inverse modelling approach indicates the robust prediction performance of the hybrid paradigms over the single techniques in both the calibration and validation steps. Whereby, the quantitative performance of the hybrid techniques, based on the Nash-Sutcliffe coefficient (NC) metric, depicts its superiority over the single paradigms by up to 58.7%/75.5% and 80.3%/51% for the calibration/validation phases in the direct and inverse modelling approaches, respectively. Moreover, to the best knowledge of the authors, this is the first study to report the implementation of direct and inverse modelling on clinical data. The findings of the current study indicates the possibility of applying these novel hybridised paradigms for the prediction of RVHF using pre-operative variables.
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Abba S, Abdulkadir R, Sammen SS, Pham QB, Lawan A, Esmaili P, Malik A, Al-Ansari N. Integrating feature extraction approaches with hybrid emotional neural networks for water quality index modeling. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108036] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Usman AG, Işik S, Abba SI, Meriçli F. Chemometrics-based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high-performance liquid chromatography. J Sep Sci 2020; 44:843-849. [PMID: 33326699 DOI: 10.1002/jssc.202000890] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 12/07/2020] [Accepted: 12/13/2020] [Indexed: 01/02/2023]
Abstract
In this research, two nonlinear models, namely; adaptive neuro-fuzzy inference system and feed-forward neural network and a classical linear model were employed for the prediction of retention time of isoquercitrin in Coriander sativum L. using the high-performance liquid chromatography technique. The prediction employed the use of composition of mobile phase and pH as the corresponding input parameters. The performance indices of the models were evaluated using root mean square error, determination co-efficient, and correlation co-efficient. The results obtained from the simple models showed that subclustering-adaptive-neuro fuzzy inference system gave the best results in both the training and testing phases and boosted the performance accuracy of the simple models. The overall comparison of the results showed that subclustering-adaptive-neuro fuzzy inference system ensemble demonstrated outstanding performance and increased the accuracy of the single models and ensemble models in the testing phase, up to 35% and 3%, respectively.
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Affiliation(s)
- Abdullahi Garba Usman
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, Turkish Republic of Northern Cyprus, Nicosia, Turkey
| | - Selin Işik
- Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, Turkish Republic of Northern Cyprus, Nicosia, Turkey
| | - Sani Isah Abba
- Department of Physical Planning Development, Maitama Sule University Kano, Kano, Nigeria
| | - Filiz Meriçli
- Department of Phytotherapy, Faculty of Pharmacy, Near East University, Turkish Republic of Northern Cyprus, Nicosia, Turkey
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