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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Devi, Seeta
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Computer Science & IT Engineering

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Innovative machine learning approaches for prediction of hypoglycemia in patients with type 2 diabetes Ramnath Gaikwad, Sachin; Devi, Seeta; Shekhar, Sameer; Dumbre, Dipali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4453-4471

Abstract

Medical data science advances using machine learning, which predicts glucose levels. A supervised machine learning technique is employed in which regression and classification methods are used to check the prediction performance. The unsupervised machine learning technique makes clusters based on variables' similarities. Furthermore, the prediction accuracy of conventional machine learning techniques is improved by proposing a transfer learning technique. Based on a median value of 67 mg/dL, the data set is divided into two groups: group 1 (BSL 57 mg/dL to 67 mg/dL) has 50.67% of the samples, and group 2 (with BSL 68 mg/dL to 79 mg/dL) has 49.33% of the samples. In regression analysis, 5-fold cross-validation is performed. The decision tree (DT) and gradient boosting (GB) individually provide a prediction accuracy of 18.2%. Regarding classification analysis, a 10-fold cross-validation configuration is used for training and testing the model. AdaBoost, GB, random forest, and neural network achieve an accuracy rate of 66.3% and an area under curve (AUC) score of 0.731. In unsupervised learning, the datasets are divided into three clusters. The clustering result is used in regression and classification models using transfer learning. The accuracy and precision of the AdaBoost and GB are as follows: 69.6%, 0.696 with f1 0.661 and 69.6%, 0.708 with f1 0.708, respectively.
Novel maternal risk factors for preeclampsia prediction using machine learning algorithms Devi, Seeta; Purushottam Bhagat, Payal; Gupta, Harshita; R., Harikrishnan; Mandrupkar, Gorakh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4544-4556

Abstract

Preeclampsia and eclampsia are the most common obstetric disorders associated with poor maternal and neonatal outcome. The study’s primary objective is to assess the accuracy of novel high-risk factors core using machine learning algorithms in predicting preeclampsia. The study included 400 pregnant women and used 27 novel high-risk factors to predict preeclampsia. The target variables for predicting preeclampsia are systolic and diastolic blood pressures. Various algorithms, including decision tree (DT), random forest (RF), gradient boosting, support vector machine (SVM), K-neighbors, light gradient boosting machine (LGBM), multi-layer perceptron (MLP), Adaboost classifier, and extra trees classifier are used in the analysis. The accuracy and precision of the LGBM classifier (0.85 and 0.9583 with F1 0.7188), support vector classifier (0.8417 and 0.92 with F1 0.7077), DT (0.825 and 0.913 with F1 0.6667), and extra trees (0.8167 and 0.9091 with F1 0.6452) are found to be better algorithms for prediction of preeclampsia. According to the novel high-risk factors score, 17.5% of pregnant women were identified as being at high risk for preeclampsia during the first trimester, which increased to 18.7% in 3rd trimester; in addition, 16% of pregnant women had a blood pressure of 140/90 mmHg and the above. Novel, high-risk scores and machine learning algorithms can effectively predict preeclampsia at an early period.
Evaluation of midwifery educated mobile applications for labor guidance and a roadmap for future developers Devi, Seeta; Rahane, Swapnil Vitthal; Podder, Lily; X., Sangeetha; Dimple, Kumari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5268-5278

Abstract

The objective of the study was to review the midwifery guided mobile apps for labor advice, assessing features, functions, and content relevance. In February to March 2024, midwifery labor-guided applications were reviewed in mobile platforms such as the Google Play Store and Apple iTunes Store. We used multimodal evaluation tools, such as the mobile app rating scale (MARS), specific statements, and IQVIA ratings, to assess the quality of these applications. The study evaluated midwifery-guided applications, resulting in an average objective quality score of 3.96±0.96 out of 5. 'Safe delivery' scored the highest rating of 4.94, followed by 'Pregnancy mentor' (4.89), 'Hypno-birthing' (4.61), 'Obstetrics 6th edition' (4.68), and 'MSD manual guide to obstetrics' (4.56). Functionality received the highest score (4.16±0.865), followed by information (3.99±0.97), engagement (3.88±1.07), and aesthetics (3.82±0.28) areas. Subjective quality score was 3.6±1.18 out of 5 for an overall MARS score of 3.76±1.02. Most applications received favorable reviews, indicating good quality, and it is recommended that future app developers design applications that include comprehensive information on labor management.
Co-Authors Dimple, Kumari Dumbre, Dipali Gupta, Harshita Mandrupkar, Gorakh Podder, Lily Purushottam Bhagat, Payal R., Harikrishnan Rahane, Swapnil Vitthal Ramnath Gaikwad, Sachin Shekhar, Sameer X., Sangeetha