Medical Research Data Research Articles

Jordanian views regarding sharing of medical data for research: A cross-sectional study during COVID-19 pandemic.

PurposeIn the current study, the views of Jordanian regarding sharing medical reports for research purposes were investigated during the COVID-19 pandemic. In addition, motivators and barriers regarding sharing of medical records were examined.MethodsThis observational survey-based cross-sectional study was conducted using an electronic questionnaire during the COVID-19 pandemic (second half of 2020). The questionnaire link was disseminated through two social media platforms (WhatsApp and Facebook), targeting Jordanian adults (age >18 years).ResultsIn this study, 1,194 participants agreed to complete the study survey. Results showed that 58.3% of them (n = 696) reported to be willing to share their medical data. while 17.6% of the participants (n = 210) showed hesitancy to share their medical information. The most important motivators as perceived by the study participants were helping other patients who have similar health conditions (n = 995, 83.3%). Moreover, fearing from stigma (n = 753, 63.1%), and the lack of confidence in data security and privacy (n = 728, 61.0%) were among the main barriers preventing participants from sharing their information. Finally, results showed that participants with higher educational level (bachelor or higher) (OR = 0.299, P<0.001), or those living in center of Jordan (OR = 0.270, P<0.001) showed a lower tendency to share their medical data. While participants those who have shared data before showed a higher tendency to share their medical data (OR = 2.524, P<0.001).ConclusionIn this study, many of the participants had a positive attitude towards sharing biomedical data for scientific research during the COVID-19 pandemic, many had doubts in the control over their data. Thus, policymakers and data users should address the concerns and values of patients and understand their preferences in favor of an ethically scrupulous use of data in research.

Medical Staff at Sanatorium-Resort Organizations: Working Conditions and Health Risk Factors

Medical staff working conditions are characterized by a range of adverse workplace factors investigated primarily at hospital organizations. However, sanitary audit of working conditions and health risk factors for medical workers in the sanatorium-resort industry is also a task that needs attention. Aim. To analyze the results of the working conditions special assessment and identify the main work-related factors affecting health state of medical staff at sanatorium-resort organizations. Material and methods. Scientific search and analysis of publications on the safety of working conditions and health protection of medical workers. Systematization of research materials, statistical and comparative analysis of the results of working conditions special assessment carried out at branches of the Medical Research Center and data provided in publications. Results. Medical staff are exposed to a wide range of hazards. Work-related factors that do not meet established hygiene standards cause the greatest concern among medical workers. These are microclimate, lighting and working equipment (47.0%, 37.8% and 24.0%, respectively). A number of devices inspected in the physiotherapy room exceeded hygienic standards for alternating electric and magnetic field intensity. Analysis of air pollution in the physiotherapy department working area showed that some of the harmful chemicals exceeded permissible exposure limits. The results of the special assessment of working conditions performed at branches of the Medical Research Center showed that a high percentage of medical workers was exposed to harmful biological factors and high severity of the labor process. Conclusion. Physical and chemical factors are the greatest workplace hazards for healthcare workers at health resorts. However, a special assessment of working conditions showed that the biological factor was the most significant one across all professional groups.

Quality assurance of integrative big data for medical research within a multihospital system

The need is growing to create medical big data based on the electronic health records collected from different hospitals. Errors for sure occur and how to correct them should be explored. Electronic health records of 9,197,817 patients and 53,081,148 visits, totaling about 500 million records for 2006-2016, were transmitted from eight hospitals into an integrated database. We randomly selected 10% of patients, accumulated the primary keys for their tabulated data, and compared the key numbers in the transmitted data with those of the raw data. Errors were identified based on statistical testing and clinical reasoning. Data were recorded in 1573 tables. Among these, 58 (3.7%) had different key numbers, with the maximum of 16.34/1000. Statistical differences (P<0.05) were found in 34 (58.6%), of which 15 were caused by changes in diagnostic codes, wrong accounts, or modified orders. For the rest, the differences were related to accumulation of hospital visits over time. In the remaining 24 tables (41.4%) without significant differences, three were revised because of incorrect computer programming or wrong accounts. For the rest, the programming was correct and absolute differences were negligible. The applicability was confirmed using the data of 2,730,883 patients and 15,647,468 patient-visits transmitted during 2017-2018, in which 10 (3.5%) tables were corrected. Significant magnitude of inconsistent data does exist during the transmission of big data from diverse sources. Systematic validation is essential. Comparing the number of data tabulated using the primary keys allow us to rapidly identify and correct these scattered errors.

Transportability and Implementation Challenges of Early Warning Scores for Septic Shock in the ICU: A Perspective on the TREWScore.

The increased use of electronic health records (EHRs) has improved the availability of routine care data for medical research. Combined with machine learning techniques this has spurred the development of early warning scores (EWSs) in hospitals worldwide. EWSs are commonly used in the hospital where they have been developed, yet few have been transported to external settings and/or internationally. In this perspective, we describe our experiences in implementing the TREWScore, a septic shock EWS, and the transportability challenges regarding domain, predictors, and clinical outcome we faced. We used data of 53,330 ICU stays from Medical Information Mart for Intensive Care-III (MIMIC-III) and 18,013 ICU stays from the University Medical Center (UMC) Utrecht, including 17,023 (31.9%) and 2,557 (14.2%) cases of sepsis, respectively. The MIMIC-III and UMC populations differed significantly regarding the length of stay (6.9 vs. 9.0 days) and hospital mortality (11.6% vs. 13.6%). We mapped all 54 TREWScore predictors to the UMC database: 31 were readily available, seven required unit conversion, 14 had to be engineered, one predictor required text mining, and one predictor could not be mapped. Lastly, we classified sepsis cases for septic shock using the sepsis-2 criteria. Septic shock populations (UMC 31.3% and MIMIC-III 23.3%) and time to shock events showed significant differences between the two cohorts. In conclusion, we identified challenges to transportability and implementation regarding domain, predictors, and clinical outcome when transporting EWS between hospitals across two continents. These challenges need to be systematically addressed to improve model transportability between centers and unlock the potential clinical utility of EWS.

Comparison of factors influencing the willingness to donate biospecimens among guardians of children with cancer and adult cancer patients.

BackgroundThis study examined and compared the attitudes and willingness of guardians of children with cancer and adult cancer patients toward donating biospecimens and clinical data for cancer research.MethodsWe conducted a cross‐sectional study among guardians of children with cancer (Guardian group) from Shanghai Children's Medical Center and adult cancer patients (Adult group) from Shanghai Ninth People's Hospital between February 1, 2019, and January 31, 2020. Participants’ demographic data, willingness, and motivations for biospecimen donation were collected and analyzed.ResultsOf 670 participants, 90.8% (318/350) in the Guardian group and 88.1% (282/320) in the Adult group completed the questionnaire. Most participants were willing to donate residual tissue samples (92.8% in the Guardian group vs. 79.4% in the Adult group, p ψ = 0.032) and clinical data (94.0% vs. 72.3%, p ψ < 0.001) for medical research. Logistic regression analysis indicated that only child status (odds ratio [OR] = 0.140, p = 0.02), history of blood donation (OR = 4.467, p = 0.019) in the Guardian group, education (OR = 0.387, p = 0.037), and history of blood donation (OR = 2.556, p = 0.016) in the Adult group were significantly associated with participants’ willingness to donate biospecimens. The primary motivation for donation was helping other patients with cancer (65.4% vs. 24.5%, p ψ < 0.001). The major barriers to donation were the potential to cause physical discomfort (61.0% vs. 64.9%, p ψ = 0.032).ConclusionsGuardians of children with cancer were more willing to donate biospecimens than adult cancer patients in China. It is essential to promote awareness of biospecimens donation, especially in adult cancer patients.

Analysis of the risk factors of mortality in elderly patients with hip fracture using a combined database of medical and long-term care insurance claims data

BackgroundThe present study evaluated the risk of mortality in elderly hip fracture, focusing on comorbidities and nursing care levels. MethodsThe present study was an observational cohort study that used a combined database of medical and long-term care insurance (LTCI) claims data from one prefecture in Japan between 2011 and 2016. In total, 6125 patients aged 65 years and older were selected from acute care hospitals with a diagnosis of “hip fracture” between March 2011 and March 2012. The impact of long-term care insurance claim evaluation levels and comorbidities at recruitment time was investigated using this dataset. These patients were followed up monthly until March 2016. Based on this person-month dataset, survival analysis was performed with death as the endpoint. Cases in which receipt data were missing during the middle of the observation period and cases in which the patient survived at the end of the observation period were censored. ResultsThe number of deaths during the observation period was 635 (10.4%). The impact of comorbidities and nursing care level on mortality were both significant as follows: high nursing care level before the fracture (hazard ratio: 1.09, P < 0.001), comorbidities of malignant diseases (HR: 1.45, P < 0.001), heart disease (hazard ratio: 1.20, P = 0.037), pneumonia (hazard ratio: 1.27, P < 0.001), chronic obstructive pulmonary disease (hazard ratio: 1.28, P = 0.026), renal failure (hazard ratio: 1.44, P < 0.001), and dementia (hazard ratio: 1.27, P = 0.013). ConclusionThe results of this study showed that a high level of nursing care and presence of comorbidities such as malignant diseases, heart diseases, pneumonia, chronic obstructive pulmonary disease, renal failure, and dementia increased mortality in elderly patients with hip fracture. Furthermore, this study showed the usefulness of a combined database of medical and LTCI claims data for clinical and health service-related research in the field of orthopedics.

Metformin and risk of age-related macular degeneration in individuals with type 2 diabetes: a retrospective cohort study

BackgroundAge-related macular degeneration (AMD) in its late stages is a leading cause of sight loss in developed countries. Some previous studies have suggested that metformin may be associated with a...

Abstract 140: Real-world Efficacy Of Left Atrial Appendage Closure Versus Anticoagulation Therapy For Long-term Prevention Of Ischemic Stroke And All-cause Mortality Among Patients With Atrial Fibrillation

Introduction: There is limited data on real-world efficacy of left atrial appendage closure (LAAC) procedures compared to anticoagulants (AC) for stroke prevention among patients with atrial fibrillation (AF). Methods: We utilized a retrospective cohort of over 63 million patients from 51 healthcare organizations across 6 countries via a harmonized electronic medical record-based research data platform. Utilizing international classification of disease version 10 and current procedural terminology codes, adult (≥ 18 years) patients with AF (I48) were identified and grouped by treatment type (LAAC (33340, 02L73DK) vs AC (1015112, Z79.0, BL110)). Patients with a history of stroke prior to AC initiation or LAAC procedure were excluded. Subjects were followed for 5 years for incident ischemic stroke (I63), intracerebral hemorrhage (I61), and all-cause mortality. Treatment groups were propensity score matched by age, sex, race, ethnicity, and comorbidities. Risk ratio(RR) and 95% confidence intervals(CI) among unmatched and matched populations are reported. Results: Among a total of 1,980,130 AF patients; 1,374,013 were flagged for AC use and 8,004 were treated via LAAC. Treatment cohorts were propensity score matched by age, race, sex, hyperlipidemia, and hypertension resulting in an exact 1:1 matched cohort of 8,004 subjects, across all co-variates. In the matched population the mean (SD) age was 75.2 (8.05) years, 63.2% were male; 86.6% were white, 4.4% black, 0.89% Asian, and 3.5% Hispanic; with 64.4% hyperlipidemic and 79.2% hypertensive. Prior to matching, AC (vs. LAAC) was significantly associated with a higher 5-year risk of ischemic stroke, intracerebral hemorrhage, and all-cause mortality. The protective effect of LAAC (vs. AC) was maintained for 5-year risk of ischemic stroke (RR, CI: 0.68, 0.58 - 0.79) and all-cause mortality (RR, CI: 0.42, 0.39 - 0.45). However, the difference in 5-year risk for intracerebral hemorrhage for LAAC (vs. AC) was not statistically lower (RR, CI: 0.72, 0.50 - 1.05). Conclusion: Notwithstanding the possibility of residual confounding in our analyses, LAAC seems to be associated with a lower long-term risk of ischemic stroke and all-cause mortality as compared to AC treated AF patients in large real-world data.

Towards a national perioperative outcomes registry: A survey of perioperative electronic medical record utilisation to support quality assurance and research at Australian and New Zealand College of Anaesthetists Clinical Trials Network hospitals in Australia.

In Australia, 2.7 million surgical procedures are performed annually. Historically, a lack of perioperative data standardisation and infrastructure has limited pooling of routinely collected data across institutions. We surveyed Australian and New Zealand College of Anaesthetists (ANZCA) Clinical Trials Network hospitals to investigate current and potential uses of perioperative electronic medical record data for research and quality assurance.A targeted survey was sent to 131 ANZCA Clinical Trials Network-affiliated hospitals in Australia. The primary aim was to map current electronic data collection methods and data utilisation in six domains of the perioperative pathway.The survey response rate was 32%. Electronic data recording in the six domains ranged from 19% to 85%. Where electronic data exist, the ability of anaesthesiology departments to export them for analysis ranged from 27% to 100%. The proportion of departments with access to data exports that are regularly exporting the data for quality assurance or research ranged from 13% to 58%.The existence of a perioperative electronic medical record does not automatically lead to the data being used to measure and improve clinical outcomes. The first barrier is clinician access to data exports. Even when this barrier is overcome, a large gap remains between the proportion of departments able to access data exports and those using the data regularly to inform and improve clinical practice. We believe this gap can be addressed by establishing a national perioperative outcomes registry to lead high-quality multicentre registry research and quality assurance in Australia.

Advances in the Application of Biological Big Data in Medicine

Advances in technology and the Internet have brought about the era of big data, and massive amounts of data have swept through almost every industry, especially in the medical field. With the penetration and expansion of information, countries around the world have started to build databases to explore the mysteries of health. In addition, the application of data storage, mining and analysis technologies in medicine has led to the involvement of biological big data in the study of many diseases. Long-term practice has revealed that combining biomacro data to analyze diseases can lead to more beneficial prevention and treatment options than conventional methods, which is a more favorable choice than ordinary methods. This review will present the progress of extensive data in medical research from the perspective of biological big data and neurodegenerative diseases (NDD), tumor, diabetes, and other applications, as well as the challenges and future directions of big data in medicine.

Data Management in Healthcare Research as a Guarantee of its Quality

Today, in times of outbreaks of epidemics such as the Zika virus and COVID-19, health systems around the globe face an urgent need to respond quickly to overcome their spread and prevent the re-infection of humanity. Among the solutions was the World Health Organization’s call for immediate, rapid, and, most importantly, open dissemination of medical research data. Although data sharing benefits science and society, there is a range of ethical, legal, cultural, financial, and technical barriers to the dissemination and reuse of medical research data today. Therefore, the quality of data, namely the possibility of their easy search, availability, compatibility, and reusability, is considered relevant in developing data infrastructure in healthcare. Consequently, there is an urgent need to develop an appropriate research data management system in the healthcare system. The aim is to study the efficiency of data management in the healthcare system. This goal is proposed to be achieved in the following sequence: to consider the essence of the concept of research data management; to conduct a bibliometric study of the concept of data management in the medical fields of knowledge, to analyze the frequency of publications on the issue of data management of medical research, indexed by the Scopus database. The object of research is the healthcare sector. The subject is the determinants of the impact of proper management of medical research data on the healthcare sector. The terminological maps of term relationships were constructed using the VOSviewer visualization tool. The study found that 8% of all data management research was conducted in the medical fields of knowledge. The study found an upward trend in the number of health data management publications with the highest growth rate in 2019 and 2020. Analysis of terminology clusters revealed that the keywords «Big Data», «Machine Learning», «Data Collection», «Data Quality», «Data Sharing», «Data Reuse», «COVID-19», and «Blockchain» have the highest number of mentions in publications and strong connection with other publications. Thus, we justified the importance of developing a coherent program and strategic plans for managing research data in the health care system. The results of the study can be used to support decision-making on future opportunities to effectively influence the development of access to medical research data, as well as to ensure the improvement of the quality and confidentiality of research data in the health system.

Privacy Preserving Attribute-Focused Anonymization Scheme for Healthcare Data Publishing

Advancements in Industry 4.0 brought tremendous improvements in the healthcare sector, such as better quality of treatment, enhanced communication, remote monitoring, and reduced cost. Sharing healthcare data with healthcare providers is crucial for harnessing the benefits of such improvements. In general, healthcare data holds sensitive information about individuals. Hence, sharing such data is challenging because of various security and privacy issues. According to privacy regulations and ethical requirements, it is essential to preserve the privacy of patients before sharing data for medical research. State-of-the-art literature on privacy preserving studies either uses cryptographic approaches to protect the privacy or uses anonymizing techniques regardless of the type of attributes, this results in poor protection and data utility. In this paper, we propose an attribute-focused privacy preserving data publishing scheme. The proposed scheme is two-fold, comprising a fixed-interval approach to protect numerical attributes and an improved l-diverse slicing approach to protect the categorical and sensitive attributes. In the fixed-interval approach, the original values of the healthcare data are replaced with an equivalent computed value. The improved l-diverse slicing approach partitions the data both horizontally and vertically to avoid privacy leaks. Extensive experiments with real-world datasets are conducted to evaluate the performance of the proposed scheme. The classification models built on anonymized dataset yields approximately 13% better accuracy than benchmarked algorithms. Experimental analyses show that the average information loss which is measured by normalized certainty penalty (NCP) is reduced by 12% compared to similar approaches. The attribute focused scheme not only provides data utility but also prevents the data from membership disclosures, attribute disclosures, and identity disclosures.

Cluster analysis of patients with granulomatosis with polyangiitis (GPA) based on clinical presentation symptoms: a UK population-based cohort study

BackgroundGranulomatosis with polyangiitis (GPA) is small vessel vasculitis with heterogeneous clinical presentation. In the present population-based cohort study, we classified patients with GPA based on clinical features at presentation using an unsupervised clustering approach and compared their mortality, infections and frequency of comorbidities.MethodsIn this open cohort study, de-identified primary care data of patients with GPA included in the IQVIA Medical Research Data database between 1 January 1995 and 25 September 2019 was analysed retrospectively. Latent class analysis was performed to create symptom clusters of patients based on 16 categories of symptoms representing various organ involvement. All-cause mortality of resultant clusters was compared after adjusting for age, sex, Townsend deprivation quintile and smoking status at index date using extended Cox proportional hazards models. Prescription of antibiotics, considered as an indirect indicator of recurrent bacterial infection, was compared using a recurrent event model, after adjusting for quarterly use of steroid as a time-dependent covariate. Cumulative frequencies of common comorbidities were compared among the clusters at index visit, 1-year and 3-year follow-up.ResultsAltogether, 649 patients with GPA [median age 60.0 (IQR: 49.6–70.1)] were included. Three clusters were identified: patients with limited disease mainly with involvement of ENT and cough were classified into cluster 1 (n = 426); cluster 2 had generalised non-renal disease (n = 176); while patients in cluster 3 had renal-predominant disease (n = 47). Many patients in cluster 1 developed generalised disease at the end of 1 year. Mortality in clusters 2 and 3 was higher compared with cluster 1. Mortality in cluster 1 itself was 68% higher than the general population without GPA. The duration of antibiotics prescription and frequency of coexisting medical illnesses was also higher in clusters 2 and 3.ConclusionsIn a primary care setting, patients with GPA can be classified into three distinct clusters with different prognosis, susceptibility to recurrent infections and presence of comorbidities. The tendency of cluster 1 to evolve into a more generalised disease raises questions about current immunosuppressive treatment approaches in these patients.

Comorbidity phenotypes and risk of mortality in patients with osteoarthritis in the UK: a latent class analysis

BackgroundOsteoarthritis (OA) is a common chronic condition but its association with other chronic conditions and mortality is largely unknown. This study aimed to use latent class analysis (LCA) of 30 comorbidities in patients with OA and matched controls without OA to identify clusters of comorbidities and examine the associations between the clusters, opioid use, and mortality.MethodsA matched cohort analysis of patients derived from the IQVIA Medical Research Data (IMRD-UK) database between 2000 and 2019. 418,329 patients with newly diagnosed OA were matched to 243,170 patients without OA to identify comorbidity phenotypes. Further analysis investigated the effect of opioid use on mortality in individuals with OA and their matched controls.ResultsThe median (interquartile range (IQR)) number of comorbidities was 2 (1–4) and 1 (0–3) in the OA and control groups respectively. LCA identified six comorbidity phenotypes in individuals with and without OA. Clusters with a high prevalence of comorbidities were characterised by hypertension, circulatory, and metabolic diseases. We identified a comorbidity cluster with the aforementioned comorbidities plus a high prevalence of chronic kidney disease, which was associated with twice the hazard of mortality in hand OA with a hazard ratio (HR) (95% CI) of 2.53 (2.05–3.13) compared to the hazard observed in hip/knee OA subtype 1.33 (1.24–1.42). The impact of opioid use in the first 12 months on hazards of mortality was significantly greater for weak opioids and strong opioids across all groups HR (95% CI) ranging from 1.11 (1.07–11.6) to 1.80 (1.69–1.92)). There was however no evidence of association between NSAID use and altered risk of mortality.ConclusionThis study identified six comorbidity clusters in individuals with OA and matched controls within this cohort. Opioid use and comorbidity clusters were differentially associated with the risk of mortality. The analyses may help shape the development of future interventions or health services that take into account the impact of these comorbidity clusters.

Data Processing Model for Compliance with International Medical Research Data Processing Rules

Data Processing Model for Compliance with International Medical Research Data Processing Rules

Brownian movement analyzed from psychophysiological measures associated with ischemic heart disease and Alzheimer’s disease

There are several coronary diseases that human beings can suffer from, which in themselves generate health deterioration and can lead to the development of other diseases that diminish the quality of life. Ischemic diseases are unique in that they are evidenced by blockages generated by the accumulation of fat that impedes circulation, triggering heart and brain-related problems. By means of fractional Brownian motion in relation to Hurst’s parameter, an analysis of a data of 137 patients aged between 30 and 71 years, who present some type of ischemic disease such as mixed, restricted, effort angina and angina pectoris, is performed. The data used was European, which is found in the PhysioNet open-access medical research data repository, managed by the Massachusetts Institute of Technology Computational Physiology Laboratory. This data shows the Hurst coefficient calculations associated with each type of ischemic heart disease.

Knowledge, attitude and practice of over-the-counter drug among undergraduate medical students-a cross sectional study

Background: Self-medication of the over-the-counter (OTC) drug is practice all over the world. Although appropriate self-medication practice can address minor illness and is time and cost-effective, it can promote irrational use of medicine; prolong the illness, drug dependency and abuse. Medical students are the future medical practitioners and play the crucial role in counseling the patients. Therefore, the present study was conducted to assess the knowledge, attitude and practice of self- medication among undergraduate students. Methods: An institution based descriptive, cross sectional study was conducted after ethical approval was taken from the Institutional ethical committee of Gandaki medical college teaching hospital and research center (GMCTHRC) and data was collected from May to July 2018. Descriptive and correlation analysis were done with the data. The results were expressed in percentages. Results: Among 115 respondents, 60.9 % (70) were female and 39.1% (45) were male students. Majority of respondents (94.8%) believed that instruction on the label of the drugs should be followed and 78.3% respondents agreed that OTC drugs could interact with other prescribed drugs. However, only 53.9% respondents knew that OTC drugs were Group Ga drugs in Nepal. Despite good knowledge and attitude only 37.4% students had good practice in the rational use of the OTC drugs. Moreover, Antipyretic and analgesic drugs were the most commonly used drugs. However, only 36.84% respondents (p&lt;0.05) knew about the correct dose and duration to take the drugs. Conclusions: We concluded that first year medical and dental should be motivated to use their knowledge into practice.

“Samples Are Precious”: Value Formations in the Potentiality and Practices of Biobanking in Singapore

Biobanking in Singapore is characterized by contested relations between funding ambitions and research practices, and different notions of what the (potential) value of storing samples and data for medical research is. Different biobanking efforts anticipate the production of public goods from stored materials in specifically situated ways. These efforts to produce public goods in the form of scientific and economic value can be fruitfully understood in terms of extraction, a complex sociotechnical process of retrieving (potential) value from raw materials, which both informs and is informed by specific social values. In exploring the extraction of potential value in relation to practice values, I propose the notion of value formations to account for the coproduction of and intersections between different forms of value(s) in scientific practices situated in particular social contexts. I trace value formations across the life span of biobanking collections, which range from recruitment, collection, and processing of samples to their storage, retrieval, and use. Observations along this life span show the social and temporal complexity of value-making in biobanking in Singapore, pointing to the contextual specificity of how biobanking is understood as a public good.

Modification of Lifestyle and Weight Loss in Curing PCOS

(PCOS) is hormonal imbalance endocrine condition which harms females aged 17-40 years of age. Women suffering from PCOS are related to obesity in the abdomen and insulin resistance. Patients do does not have to be obese all the time. Women with PCOS suffer from fertility issues, hyperandrogenaemia, heavy menstrual flow, and anovulation, which is a sign of PCOS. It leads to insulin dysfunction, low metabolism, and chronic low-grade swelling. As per the Indian Council Of medical research (ICMR) data, 12.2% of adolescents in India are suffering from PCOS. Women with PCOS also suffer from anxiety, depression, lower self-esteem, and body dissatisfaction, leading to poor lifestyles. A systemic approach to a balanced diet with exercise helps in improving endocrine disorder, reproductive problems, and cardiovascular disease even without shedding much weight. Fat intake should be limited to &lt; or approximately 30% of total calorie intake with less hydrogenated fat. Excessive carbohydrate intake having a low glycaemic index gives rise to dyslipidemia and hence leads to an increase in overall body weight with an increase in appetite, which provokes hunger and desire for carbohydrates. Energy consumption should be spread out among numerous meals each day, reducing mid-day beverages and snacks. It is advised to use drugs to regulate insulin sensitivity and elevate loss in body weight which can benefit only when used early with combined exercise and diet.

Statistical Machine Learning Approaches to Liver Disease Prediction

Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data (ntrain=399) for learning and testing data (ntest=216) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed (p&lt;0.001) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.