Data Mining Classification Approach to Predict The Duration of Contraceptive Use

Maulana, Yudhi Dwi Fajar; Ruldeviyani, Yova; Sensuse, Dana Indra

doi:10.1109/icic50835.2020.9288568

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…In contrast to previous studies conducted by Yudhi Dwi Fajar Maulana [27], our study focuses on a different population and context, employing a distinct set of machine learning algorithms. While Yudhi Dwi Fajar Maulana's study addresses the duration of contraceptive use among productive couples in Indonesia using data mining techniques and achieves an accuracy score of 85.1% for the Adaboost model, our study explores the prediction of contraceptive usage among married African women residing in rural areas using a combination of machine learning and deep learning models.…”

Section: Discussionmentioning

confidence: 99%

Predicting Contraceptive Usage for Married African Women Residing in Rural Areas: A Comparative Study of Deep Learning and Machine Learning Models with XAI Insights

Nuri,

Kanda,

Justine

et al. 2023

Preprint

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Background The use of modern contraceptives is a crucial aspect of family planning, especially for women residing in rural areas of Africa. This study seeks to address the issue of contraceptive usage among married African women living in rural areas by exploring the effectiveness of machine learning and deep learning models for predicting this usage. Methodology The data used in the study was obtained from the Multiple Indicator Cluster Survey 6 (MICS6) to develop and compare machine learning and deep learning models for predicting contraceptive usage among married African women residing in rural areas. In addition to predictive accuracy, the study also focused on incorporating explainable Artificial Intelligence (XAI) insights to enhance the interpretability of the models. Result Artificial Neural Network (ANN) with BFloat16 and Convolutional Neural Network (CNN) + Long Short-Term Memory (LSTM) were the best predictive models with 75% accuracy in both models and F1-score of 73% and 74% respectively. Additionally, XAI techniques provide valuable insights into the factors that influence contraceptive usage in the target population. Conclusion The results of this study indicate that deep learning models outperform traditional machine learning models in predicting contraceptive usage among married African women residing in rural areas. The implications of this research are significant, as the findings could inform policy and intervention strategies aimed at improving family planning services in rural areas of Africa.

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Section: Discussionmentioning

confidence: 99%

Predicting Contraceptive Usage for Married African Women Residing in Rural Areas: A Comparative Study of Deep Learning and Machine Learning Models with XAI Insights

Nuri,

Kanda,

Justine

et al. 2023

Preprint

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“…Another study was conducted to examine in detail how a particular data mining method called General Unary Hypotheses Automaton (GUHA) helps predict women's use of contraceptive methods based on knowledge of their demographic and socioeconomic characteristics [55]. Another study was also conducted using data mining classi cation algorithm to predict the duration of contraceptive use of productive couples by adopting the CRISP-DM process method [59]. And data mining techniques were employed to different experimentations using Demography and Health Survey of Indonesia (DHSI) in 2017.…”

Section: B Application Of Data Mining Models To Contraceptive Usementioning

confidence: 99%

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

Hailu

Abdulkadir²

2023

Preprint

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In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user's requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.

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Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS

Zemariam,

Abey,

Kassaw

et al. 2024

Health Informatics J

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Background: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictive performance of ML algorithms for diarrhea in under-5 children in Ethiopia. Methods: The study utilized a dataset of 9501 under-5 children from the Ethiopia Demographic and Health Survey 2016. Five ML algorithms were used to build and compare predictive models. The model performance was evaluated using various metrics in Python. Boruta feature selection was employed, and data balancing techniques such as under-sampling, over-sampling, adaptive synthetic sampling, and synthetic minority oversampling as well as hyper parameter tuning methods were explored. Association rule mining was conducted using the Apriori algorithm in R to determine relationships between independent and target variables. Results: 10.2% of children had diarrhea. The Random Forest model had the best performance with 93.2% accuracy, 98.4% sensitivity, 85.5% specificity, and 0.916 AUC. The top predictors were residence, wealth index, and child age, number of living children, deworming, wasting, mother’s occupation, and education. Association rule mining identified the top 7 rules most associated with under-5 diarrhea in Ethiopia. Conclusion: The RF achieved the highest performance for predicting childhood diarrhea. Policymakers and healthcare providers can use these findings to develop targeted interventions to reduce diarrhea. Customizing strategies based on the identified association rules has the potential to improve child health and decrease the impact of diarrhea in Ethiopia.

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Data Mining Classification Approach to Predict The Duration of Contraceptive Use

Cited by 4 publications

References 8 publications

Predicting Contraceptive Usage for Married African Women Residing in Rural Areas: A Comparative Study of Deep Learning and Machine Learning Models with XAI Insights

Predicting Contraceptive Usage for Married African Women Residing in Rural Areas: A Comparative Study of Deep Learning and Machine Learning Models with XAI Insights

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS

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