Weighted Bayesian Belief Network for diabetics: a predictive model.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules.

In this current work, Weighted Bayesian Association rules using the Fuzzy set theory are proposed with the new concept of Fuzzy Weighted Bayesian Association Rules to design and develop a Clinical Decision Support System on the Bayesian Belief Network, which is an appropriate area to work in Clinical Domain as it has a higher degree of unpredictability and causality. Weighted Bayesian Association rules to construct a Bayesian network are already proposed. A "Sharp boundary" issue related to quantitative attribute domains may cause erroneous predictions in medicine and treatment in the medical environment. So to eradicate sharp boundary problems in the medical field, the fuzzy theory is applied in attributes to deal with real-life situations. A new algorithm is designed and implemented in this paper to set up a new Bayesian belief network using the concept of Fuzzy Weighted Association rule mining under the Predictive Modeling paradigm named Fuzzy weighted Bayesian belief network using numerous clinical datasets with outshone results.

Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets.

There are growing concerns about the mortality due to Breast cancer many of which often result from delayed detection and treatment. So an effective computational approach is needed to develop a predictive model which will help patients and physicians to manage the situation timely. This study presented a Weighted Bayesian Belief Network (WBBN) modeling for breast cancer prediction using the UCI breast cancer dataset. New automated ranking method was used to assign proper weights to attribute value pair based on their impact on causing the disease. Association between attributes was generated using weighted association rule mining between two attributes, multiattributes, and with class labels to generate rules. Weighted Bayesian confidence and weighted Bayesian lift measures were used to produce strong rules to build the model. To build WBBN, the Open Markov tool was used for structure and parametric learning using generated strong rules. The model was trained using 70% records and tested on 30% records with a threshold value of minimum support = 36% and confidence = 70% which produced results with an accuracy of 97.18%. Experimental results show that WBBN achieved better results in most cases compared to other predictive models. The study would contribute to the fight against breast cancer and the quality of treatment.

Time series forecasting for uni- variant data using hybrid GA-OLSTM model and performance evaluations.

Time series forecasting of uni-variant rainfall data is done using a hybrid genetic algorithm integrated with optimized long-short term memory (GA-OLSTM) model. The parameters included for the valuation of the efficiency of the considered model, were mean square error (MSE), root mean square error (RMSE), cosine similarity (CS) and correlation coefficient (r). With various epochs like 5, 10, 15 and 20, the optimal window size and the number of units were observed using the GA search algorithm which was found to be (49, 9), (12, 8), (40, 8), and (36, 2) respectively. The computed MSE, RMSE, CS and r for 10 epochs were found to be 0.006, 0.078, 0.910 and 0.858 respectively for the LSTM model, whereas the same parameters were computed using the Hybrid GA-OLSTM model was 0.004, 0.063, 0.947 and 0.917 respectively. The experimental results expressed that the Hybrid GA-OLSTM model gave significantly better results comparing the LSTM model for 10 epochs has been discussed in this research article.

Negotiating Collective and Individual Agency: A Qualitative Study of Young Women's Reproductive Health in Rural India.

The societal changes in India and the available variety of reproductive health services call for evidence to inform health systems how to satisfy young women's reproductive health needs. Inspired by Foucault's power idiom and Bandura's agency framework, we explore young women's opportunities to practice reproductive agency in the context of collective social expectations. We carried out in-depth interviews with 19 young women in rural Rajasthan. Our findings highlight how changes in notions of agency across generations enable young women's reproductive intentions and desires, and call for effective means of reproductive control. However, the taboo around sex without the intention to reproduce made contraceptive use unfeasible. Instead, abortions were the preferred method for reproductive control. In conclusion, safe abortion is key, along with the need to address the taboo around sex to enable use of "modern" contraception. This approach could prevent unintended pregnancies and expand young women's agency.

Acceptability of Home-Assessment Post Medical Abortion and Medical Abortion in a Low-Resource Setting in Rajasthan, India. Secondary Outcome Analysis of a Non-Inferiority Randomized Controlled Trial.

BACKGROUND: Studies evaluating acceptability of simplified follow-up after medical abortion have focused on high-resource or urban settings where telephones, road connections, and modes of transport are available and where women have formal education. OBJECTIVE: To investigate women's acceptability of home-assessment of abortion and whether acceptability of medical abortion differs by in-clinic or home-assessment of abortion outcome in a low-resource setting in India. DESIGN: Secondary outcome of a randomised, controlled, non-inferiority trial. SETTING: Outpatient primary health care clinics in rural and urban Rajasthan, India. POPULATION: Women were eligible if they sought abortion with a gestation up to 9 weeks, lived within defined study area and agreed to follow-up. Women were ineligible if they had known contraindications to medical abortion, haemoglobin < 85 mg/l and were below 18 years. METHODS: Abortion outcome assessment through routine clinic follow-up by a doctor was compared with home-assessment using a low-sensitivity pregnancy test and a pictorial instruction sheet. A computerized random number generator generated the randomisation sequence (1:1) in blocks of six. Research assistants randomly allocated eligible women who opted for medical abortion (mifepristone and misoprostol), using opaque sealed envelopes. Blinding during outcome assessment was not possible. MAIN OUTCOME MEASURES: Women's acceptability of home-assessment was measured as future preference of follow-up. Overall satisfaction, expectations, and comparison with previous abortion experiences were compared between study groups. RESULTS: 731 women were randomized to the clinic follow-up group (n = 353) or home-assessment group (n = 378). 623 (85%) women were successfully followed up, of those 597 (96%) were satisfied and 592 (95%) found the abortion better or as expected, with no difference between study groups. The majority, 355 (57%) women, preferred home-assessment in the event of a future abortion. Significantly more women, 284 (82%), in the home-assessment group preferred home-assessment in the future, as compared with 188 (70%) of women in the clinic follow-up group, who preferred clinic follow-up in the future (p < 0.001). CONCLUSION: Home-assessment is highly acceptable among women in low-resource, and rural, settings. The choice to follow-up an early medical abortion according to women's preference should be offered to foster women's reproductive autonomy. TRIAL REGISTRATION: ClinicalTrials.gov NCT01827995.

Self-assessment of the outcome of early medical abortion versus clinic follow-up in India: a randomised, controlled, non-inferiority trial.

BACKGROUND: The need for multiple clinical visits remains a barrier to women accessing safe legal medical abortion services. Alternatives to routine clinic follow-up visits have not been assessed in rural low-resource settings. We compared the effectiveness of standard clinic follow-up versus home assessment of outcome of medical abortion in a low-resource setting. METHODS: This randomised, controlled, non-inferiority trial was done in six health centres (three rural, three urban) in Rajasthan, India. Women seeking early medical abortion up to 9 weeks of gestation were randomly assigned (1:1) to either routine clinic follow-up or self-assessment at home. Randomisation was done with a computer-generated randomisation sequence, with a block size of six. The study was not blinded. Women in the home-assessment group were advised to use a pictorial instruction sheet and take a low-sensitivity urine pregnancy test at home, 10-14 days after intake of mifepristone, and were contacted by a home visit or telephone call to record the outcome of the abortion. The primary (non-inferiority) outcome was complete abortion without continuing pregnancy or need for surgical evacuation or additional mifepristone and misoprostol. The non-inferiority margin for the risk difference was 5%. All participants with a reported primary outcome and who followed the clinical protocol were included in the analysis. This study is registered with ClinicalTrials.gov, number NCT01827995. FINDINGS: Between April 23, 2013, and May 15, 2014, 731 women were recruited and assigned to clinic follow-up (n=366) or home assessment (n=365), of whom 700 were analysed for the main outcomes (n=336 and n=364, respectively). Complete abortion without continuing pregnancy, surgical intervention, or additional mifepristone and misoprostol was reported in 313 (93%) of 336 women in the clinic follow-up group and 347 (95%) of 364 women in the home-assessment group (difference -2·2%, 95% CI -5·9 to 1·6). One case of haemorrhage occurred in each group (rate of adverse events 0·3% in each group); no other adverse events were noted. INTERPRETATION: Home assessment of medical abortion outcome with a low-sensitivity urine pregnancy test is non-inferior to clinic follow-up, and could be introduced instead of a clinic follow-up visit in a low-resource setting.