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Noman SM, Zeeshan M, Arshad J, Deressa Amentie M, Shafiq M, Yuan Y, Zeng M, Li X, Xie Q, Jiao X. Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:5236168. [PMID: 36909968 PMCID: PMC9995192 DOI: 10.1155/2023/5236168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/21/2022] [Accepted: 02/02/2023] [Indexed: 03/05/2023]
Abstract
AIM Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa. METHOD A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis. RESULTS Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was 99.31% and 94.00%, amoxicillin was 99.02% and 95.21%, meropenem was 98.27% and 96.63%, cefepime was 99.73% and 98.34%, fosfomycin was 96.44% and 99.23%, ceftazidime was 98.63% and 94.31%, chloramphenicol was 98.71% and 96.00%, erythromycin was 95.76% and 97.63%, tetracycline was 99.27% and 98.25%, gentamycin was 98.00% and 97.30%, butirosin was 99.57% and 98.03%, and ciprofloxacin was 96.17% and 98.97% with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains. CONCLUSION The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.
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Affiliation(s)
- Sohail M. Noman
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Muhammad Zeeshan
- Department of Medicine and Surgery, Al-Nafees Medical College and Hospital, Isra University, Islamabad 44000, Pakistan
| | - Jehangir Arshad
- Department of Electrical and Computer Engineering, Comsats University Islamabad, Lahore Campus 44000, Lahore, Pakistan
| | | | - Muhammad Shafiq
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Yumeng Yuan
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Mi Zeng
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Xin Li
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Qingdong Xie
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
| | - Xiaoyang Jiao
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, China
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An Intelligent Cost-Efficient System to Prevent the Improper Posture Hazards in Offices Using Machine Learning Algorithms. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7957148. [PMID: 36035860 PMCID: PMC9410927 DOI: 10.1155/2022/7957148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/27/2022] [Accepted: 07/14/2022] [Indexed: 11/17/2022]
Abstract
In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid the harms of an improper posture as it, when prolonged, is very painful and can be fatal sometimes. This study also includes a comparison of two arrangements. Arrangement 01 includes six force-sensitive resistor (FSR) sensors alone, and it is less expensive. Arrangement 02 consists of two FSR sensors and one ultrasonic sensor embedded in the back seat of a chair. The K-nearest neighbor (KNN), Naive Bayes, logistic regression, and random forest algorithms are used to augment the gain and enhanced accuracy for posture detection. The improper postures recognized in this study are backward-leaning, forward-leaning, left-leaning, and right-leaning. The presented results validate the proposed system as the accuracy of 99.8% is achieved using a smaller number of sensors that make the proposed prototype cost-efficient with improved accuracy and lower execution time. The proposed model is of a dire need for employees working in offices or even at the residential level to make it convenient to work for hours without having severe effects of improper posture and prolonged sitting.
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Noman SM, Arshad J, Zeeshan M, Rehman AU, Haider A, Khurram S, Cheikhrouhou O, Hamam H, Shafiq M. An Empirical Study on Diabetes Depression over Distress Evaluation Using Diagnosis Statistical Manual and Chi-Square Method. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18073755. [PMID: 33916851 PMCID: PMC8038424 DOI: 10.3390/ijerph18073755] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/19/2021] [Accepted: 03/23/2021] [Indexed: 02/05/2023]
Abstract
Diabetes distress is an alternative disorder that is often associated with depression syndromes. Psychosocial distress is an alternative disorder that acts as a resistance to diabetes self-care management and compromises diabetes control. Yet, in Nigeria, the focus of healthcare centers is largely inclined toward the medical aspect of diabetes that neglects psychosocial care. In this retrospective study, specific distress was measured by the Diabetes Distress Screening (DDS) scale, and depression was analyzed by the Beck Depression Inventory (BDI) and Diagnosis Statistics Manual (DSM) criteria in type 2 diabetes mellitus (T2DM) patients of Northwestern Nigeria. Additionally, we applied the Chi-square test and linear regression to measure the forecast prevalence ratio and evaluate the link between the respective factors that further determine the odd ratios and coefficient correlations in five nonintrusive variables, namely age, gender, physical exercise, diabetes history, and smoking. In total, 712 sample patients were taken, with 51.68% male and 47.31% female patients. The mean age and body mass index (BMI) was 48.6 years ± 12.8 and 45.6 years ± 8.3. Based on the BDI prediction, 90.15% of patients were found depressed according to the DSM parameters, and depression prevalence was recorded around 22.06%. Overall, 88.20% of patients had DDS-dependent diabetes-specific distress with a prevalence ratio of 24.08%, of whom 45.86% were moderate and 54.14% serious. In sharp contrast, emotion-related distress of 28.96% was found compared to interpersonal (23.61%), followed by physician (16.42%) and regimen (13.21%) distress. The BDI-based matching of depression signs was also statistically significant with p < 0.001 in severe distress patients. However, 10.11% of patients were considered not to be depressed by DSM guidelines. The statistical evidence indicates that depression and distress are closely correlated with age, sex, diabetes history, physical exercise, and smoking influences. The facts and findings in this work show that emotional distress was found more prevalent. This study is significant because it considered several sociocultural and religious differences between Nigeria and large, undeveloped, populated countries with low socioeconomic status and excessive epidemiological risk. Finally, it is important for the clinical implications of T2DM patients on their initial screenings.
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Affiliation(s)
- Sohail M. Noman
- Department of Cell Biology and Genetics, Shantou University Medical College, Shantou 515041, China;
| | - Jehangir Arshad
- Department of Electrical & Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan;
| | - Muhammad Zeeshan
- Department of Medicine and Surgery, Al-Nafees Medical College and Hospital, Isra University, Islamabad 44000, Pakistan;
| | - Ateeq Ur Rehman
- Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan;
| | - Amir Haider
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea;
| | - Shahzada Khurram
- Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan;
| | - Omar Cheikhrouhou
- College of CIT, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Habib Hamam
- Faculty of Engineering, Moncton University, Moncton, NB E1A3E9, Canada;
| | - Muhammad Shafiq
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
- Correspondence:
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Raja JB, Pandian SC. PSO-FCM based data mining model to predict diabetic disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105659. [PMID: 32698060 DOI: 10.1016/j.cmpb.2020.105659] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/07/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Diabetic disease is typically composed because of higher than normal blood sugar levels. Instead the production of insulin may be regarded insufficient. It has been noted in recent days that the percentage of diabetes-affected patients have grown to a larger extent throughout the world. Evidently, this problem must be taken more seriously in the coming days to ensure that the average percentages of diabetes-affected individuals are reduced. Recently, several research teams conducted detailed research on the data mining platform to determine the precision of each other. Data mining can be used by parametric modeling from the health data, including diabetic patient data sets, to synthesize expertise in the field. METHODS In this study, a new model is proposed for forecasting type 2 diabetes mellitus (T2DM) based on data mining strategies. The combined Particle Swarm Optimization (PSO) and Fuzzy Clustering Means (FCM) (PSO-FCM) are used to evaluate a set of medical data relating to a diabetes diagnosis challenge. RESULTS Experiments are performed on the Pima Indians Diabetes Database. The sensitivity, specificity and accuracy metrics widely used in medical studies have been used to assess the effectiveness of the proposed system reliability. It was found that the prototype has achieved 8.26 percent more accuracy than the other methods. CONCLUSION The conclusion produced by using the method shows that, as compared with other models, the proposed PSO-FCM method delivers greater performance.
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Affiliation(s)
- J Beschi Raja
- Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
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Muhammad MU, Jiadong R, Muhammad NS, Nawaz B. Stratified Diabetes Mellitus Prevalence for the Northwestern Nigerian States, a Data Mining Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:E4089. [PMID: 31652912 PMCID: PMC6928643 DOI: 10.3390/ijerph16214089] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 10/20/2019] [Accepted: 10/21/2019] [Indexed: 01/09/2023]
Abstract
An accurate classification for diabetes mellitus (DBM) allows for the adequate treatment and handling of its menace, particularly in developing countries like Nigeria. This study proposes data mining techniques for the classification and identification of the prevalence of diagnosed diabetes cases, stratified by age, gender, diabetic conditions and residential area in the northwestern states of Nigeria, based on the real-life data derived from government-owned hospitals in the region. A K-mean assessment was used to cluster the instances, after 12 iterations the instances classified out of 3022: 2662 (88.09%) non-insulin dependent (NID), 176 (5.82%) insulin-dependent (IND) and 184 (6.09%) gestational diabetes (GTD). The total number of diagnosed diabetes cases was 3022: 1380 males (45.66%) and 1642 females (54.33%). The higher prevalence was found to be in females compared to males, and in cities and towns, rather than in villages (36.5%, 34.2%, and 29.3%, respectively). The highest prevalence among the age groups was in the age group 50-69 years, which constituted 43.9% of the total diagnosed cases. Furthermore, the NID condition had the highest prevalence of cases (88.09%). These were the first findings of the stratified prevalence in the region, and the figures have been of utmost significance to the healthcare authorities, policymakers, clinicians, and non-governmental organizations for the proper planning and management of diabetes mellitus.
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Affiliation(s)
- Musa Uba Muhammad
- Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China.
| | - Ren Jiadong
- Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China.
| | - Noman Sohail Muhammad
- Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China.
| | - Bilal Nawaz
- State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao 066004, China.
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