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Fulltext Prevalence of overactive bladder syndrome (OABS) among women with gynaecological problems and its risk factors in a tertiary hospital, Negeri Sembilan, Malaysia: Implication for primary healthcare providers

Ahmad SM, Aznal SS, Tham SW

Malays Fam Physician, 2015;10(2):2-8.
PMID: 27099656 MyJurnal

The objective of this paper was to determine the prevalence of overactive bladder syndrome (OABS) and its risk factors among patients with other gynaecological problems.
Fulltext Online handwritten signature verification using neural network classifier based on principal component analysis

Iranmanesh V, Ahmad SM, Adnan WA, Yussof S, Arigbabu OA, Malallah FL

ScientificWorldJournal, 2014;2014:381469.
PMID: 25133227 DOI: 10.1155/2014/381469

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.
Fulltext Estimating body related soft biometric traits in video frames

Arigbabu OA, Ahmad SM, Adnan WA, Yussof S, Iranmanesh V, Malallah FL

ScientificWorldJournal, 2014;2014:460973.
PMID: 25121120 DOI: 10.1155/2014/460973

Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.
Analysis of the effects and relationship of perceived handwritten signature's size, graphical complexity, and legibility with dynamic parameters for forged and genuine samples

Ahmad SM, Ling LY, Anwar RM, Faudzi MA, Shakil A

J Forensic Sci, 2013 May;58(3):724-31.
PMID: 23527753 DOI: 10.1111/1556-4029.12075

This article presents an analysis of handwritten signature dynamics belonging to two authentication groups, namely genuine and forged signature samples. Genuine signatures are initially classified based on their relative size, graphical complexity, and legibility as perceived by human examiners. A pool of dynamic features is then extracted for each signature sample in the two groups. A two-way analysis of variance (ANOVA) is carried out to investigate the effects and the relationship between the perceived classifications and the authentication groups. Homogeneity of variance was ensured through Bartlett's test prior to ANOVA testing. The results demonstrated that among all the investigated dynamic features, pen pressure is the most distinctive which is significantly different for the two authentication groups as well as for the different perceived classifications. In addition, all the relationships investigated, namely authenticity group versus size, graphical complexity, and legibility, were found to be positive for pen pressure.
Fulltext Assessment of predictive models for chlorophyll-a concentration of a tropical lake

Malek S, Syed Ahmad SM, Singh SK, Milow P, Salleh A

BMC Bioinformatics, 2011;12 Suppl 13:S12.
PMID: 22372859 DOI: 10.1186/1471-2105-12-S13-S12

This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.
Wavelet Analysis of Resultant Velocity Belonging to Genuine and Forged Signatures

Syed Ahmad SM, Loo LY, Wan Adnan WA, Md Anwar R

J Forensic Sci, 2017 Mar;62(2):374-381.
PMID: 28000207 DOI: 10.1111/1556-4029.13303

This study presents a wavelet analysis of resultant velocity features belonging to genuine and forged groups of signature sample. Signatures of individuals were initially classified based on visual human perceptions of their relative sizes, complexities, and legibilities of the genuine counterparts. Then, the resultant velocity was extracted and modeled through wavelet analysis from each sample. The wavelet signal was decomposed into several layers based on maximum overlap discrete wavelet transform (MODWT). Next, the zero crossing rate features were calculated from all the high wavelet sub-bands. A total of seven hypotheses were then tested using a two-way ANOVA testing methodology. Of these, four hypotheses were conducted to test for significance differences between distributions. In addition, three hypotheses were run to provide test for interaction between two factors of signature authentication versus perceived classification. The results demonstrated that both feature distributions belonging to genuine and forged groups of samples cannot be distinguished by themselves. Instead, they were significantly different under the influence of two other inherent factors, namely perceived size and legibility. Such new findings are useful information particularly in providing bases for forensic justifications in establishing the authenticity of handwritten signature specimens.
Ovarian tissue cryopreservation in Malaysia: a case series

Mat Jin N, Ahmad SM, Mohd Faizal A, Abdul Karim AKB, Abu MA

Horm Mol Biol Clin Investig, 2022 Dec 01;43(4):469-474.
PMID: 35545610 DOI: 10.1515/hmbci-2021-0096

OBJECTIVES: We aim to discuss the hematological cancer cases that opted for ovarian tissue cryopreservation (OTC) as fertility preservation before the gonadotoxic chemotherapy agent.
CASE PRESENTATION: The ovarian tissue cryopreservation (OTC) was started in August 2020 in our center. Up to now, there were four cases have been performed and included in this report. The ovarian tissue cortex was cryopreserved with cryoprotectant using Kitazato™ (Tokyo, Japan) media and fit in the closed system devices. A total of four post-OTC patients were included. The mean age was 24 years old, whereas the mean serum AMH level was 30.43 pmol/L. Most of them were diagnosed with lymphoma, except one was leukemia. All of them received additional GnRH analog following OTC as a chemoprotective agent before cancer treatment. Currently, they are recovering well and on regular follow-up with the hematological department.
CONCLUSIONS: Although The OTC is an ultimate option for prepubertal girls, it can be proposed as a good strategy for adult cancer women who could not delay cancer therapy.
Unexplained subfertility: active or conservative management?

Ahmad SM, Mat Jin N, Ahmad MF, Abdul Karim AK, Abu MA

Horm Mol Biol Clin Investig, 2023 Dec 01;44(4):379-384.
PMID: 38124670 DOI: 10.1515/hmbci-2022-0087

OBJECTIVES: Unexplained subfertility (UEI) describes a couple whose standard subfertility workout consider acceptable but unable to conceived.
METHODS: This retrospective study was conducted in the Advanced Reproductive Centre, UKM Hospital, Kuala Lumpur, from January 2016 to December 2019. The data of 268 UEI couples were obtained from the clinical database. Women aged 21-45 years old was included and further divided into four groups according to the female partner's age and subfertility duration: group A (age <35 years and subfertility <2 years), group B (age <35 years and subfertility >2 years), group C (age >35 years and subfertility <2 years), and group D (age >35 years and subfertility <2 years). All statistical analyses were performed using SPSS 22.0 for Windows.
RESULTS: A total of 255 cases were included in this study. The mean age of the women was 32.9 ± 4.04 years, and the mean subfertility duration was 5.04 ± 2.9 years. A total of 51 (20 %) cases underwent timed sexual intercourse, 147 (57.6 %) cases had intrauterine insemination (IUI), whereas 57 (22.4 %) cases opted for in vitro fertilization (IVF). A total of 204 cases underwent active management (IUI/IVF), which showed a significant difference (p<0.05). Out of eight clinical pregnancies, half of them were from group B.
CONCLUSIONS: Active management in younger women with a shorter subfertility duration revealed a better pregnancy outcome. Otherwise, individualized treatment should be considered in selecting a suitable treatment plan.
Machine learning-based risk prediction model for medication administration errors in neonatal intensive care units: A prospective direct observational study

Henry Basil J, Lim WH, Syed Ahmad SM, Menon Premakumar C, Mohd Tahir NA, Mhd Ali A, et al.

Digit Health, 2024;10:20552076241286434.
PMID: 39430694 DOI: 10.1177/20552076241286434

OBJECTIVE: Neonates' physiological immaturity and complex dosing requirements heighten their susceptibility to medication administration errors (MAEs), with the potential for severe harm and substantial economic impact on healthcare systems. Developing an effective risk prediction model for MAEs is crucial to reduce and prevent harm.
METHODS: This national-level, multicentre, prospective direct observational study was conducted in neonatal intensive care units (NICUs) of five public hospitals in Malaysia. Randomly selected nurses were directly observed during medication preparation and administration. Each observation was independently assessed for errors. Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and expert consensus. Model performance, prioritising F1-score for MAEs, was evaluated using various measures. Feature importance was determined using the permutation-feature importance for robust comparison across ML algorithms.
RESULTS: A total of 1093 doses were administered to 170 neonates, with mean age and birth weight of 33.43 (SD ± 5.13) weeks and 1.94 (SD ± 0.95) kg, respectively. F1-scores for the ten models ranged from 76.15% to 83.28%. Adaptive boosting (AdaBoost) emerged as the best-performing model (F1-score: 83.28%, accuracy: 77.63%, area under the receiver operating characteristic: 82.95%, precision: 84.72%, sensitivity: 81.88% and negative predictive value: 64.00%). The most influential features in AdaBoost were the intravenous route of administration, working hours, and nursing experience.
CONCLUSIONS: This study developed and validated an ML-based model to predict the presence of MAEs among neonates in NICUs. AdaBoost was identified as the best-performing algorithm. Utilising the model's predictions, healthcare providers can potentially reduce MAE occurrence through timely interventions.