Data Mining Practical Machine Learning Tools and Techniques

Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In this study we consider 24 predictive parameters and create a machine learning classifier to detect CKD. We evaluate our approach on a dataset of 400 individuals, where 250 of them have CKD. Using our approach we achieve a detection accuracy of 0.993 according to the F1-measure with 0.1084 root mean square error. This is a 56% reduction of mean square error compared to the state of the art (i.e., the CKD-EPI equation: a glomerular filtration rate estimator). We also perform feature selection to determine the most relevant attributes for detecting CKD and rank them according to their predictability. We identify new predictive attributes which have not been used by any previous GFR estimator equations. Finally, we perform a cost-accuracy tradeoff analysis to identify a new CKD detection approach with high accuracy and low cost.

Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes

In this digital world, data is an asset, and enormous data was generating in all the fields. Data in the healthcare industry consists of patient information and disease-related information. This medical data and machine learning techniques will help us to analyse a large amount of data to find out the hidden patterns in the disease, to provide personalised treatment for the patient and also used to predict the disease. In this work, a general architecture has proposed for predicting the disease in the healthcare industry. This system was experimented using with reduced set features of Chronic Kidney Disease, Diabetes and Heart Disease dataset using improved SVM-Radial bias kernel method, and also this system has compared with other machine learning techniques such as SVM-Linear, SVM-Polynomial, Random forest and Decision tree in R studio. The performance of all these machine learning algorithms has evaluated with accuracy, misclassification rate, precision, sensitivity and specificity. From the experiment results, improved SVM-Radial bias kernel technique produces accuracy as 98.3%, 98.7% and 89.9% in Chronic Kidney Disease, Diabetes and Heart Disease dataset respectively.

Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system

Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.

/pdf/medical-diagnostic-systems-using-artificial-intelligence-ai-596qei4se1.pdf

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Chronic kidney disease (CKD), also known as chronic renal disease, which is progressive loss in kidney function over a period of months or years. It is defined by the presence of kidney damage or decreased glomerular filtration rate (GFR). The estimated prevalence of CKD is about 9–13% in the general adult population. Individuals with CKD have a far greater likelihood of cardiovascular death than progression to end-stage renal disease. CKD is more prevalent in patients with CVD or with CVD related risk factors, such as hypertension, diabetes mellitus, dyslipidemia, and metabolic syndrome In proposed work, we are not only extracting action rules based on stages but also predicting CKD by using naive bayes with OneR attribute selector which helps to prevent the advancing of chronic renal disease to further stages.

Extraction of action rules for chronic kidney disease using Naïve bayes classifier

Chronic kidney disease (CKD), also known as chronic renal disease. Chronic kidney disease involves conditions that damage your kidneys and decrease their ability to keep you healthy. You may develop complications like high blood pressure, anemia (low blood count), weak bones, poor nutritional health and nerve damage. . Early detection and treatment can often keep chronic kidney disease from getting worse. Data Mining is the term used for knowledge discovery from large databases. The task of data mining is to make use of historical data, to discover regular patterns and improve future decisions, follows from the convergence of several recent trends: the lessening cost of large data storage devices and the everincreasing ease of collecting data over networks; the expansion of robust and efficient machine learning algorithms to process this data; and the lessening cost of computational power, enabling use of computationally intensive methods for data analysis. Machine learning, has already created practical applications in such areas as analyzing medical science outcomes, detecting fraud, detecting fake users etc. Various data mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. The objective of this research work is to introduce a new decision support system to predict chronic kidney disease. The aim of this work is to compare the performance of Support vector machine (SVM) and K-Nearest Neighbour (KNN) classifier on the basis of its accuracy, precision and execution time for CKD prediction. From the experimental results it is observed that the performance of KNN classifier is better than SVM. Keywords—Data Mining, Machine learning, Chronic kidney disease, Classification, K-Nearest Neighbour, Support vector machine.

/pdf/comparative-study-of-chronic-kidney-disease-prediction-using-1q4wnezneq.pdf

Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM

Indian mobile industry is the most rapidly expanding businesses in the world. Presently, India is one of the second world’s largest telecommunication market and is still mounting. The main aim of this review paper is to explore the factors affecting towards the customer retention from the customers prospective in line with the experience of mobile connections to our country. The present study conducts a systematic review of 50 research papers published across reference peer reviewed journals during 2001-2019 in telecom sector. During the analysis of the literature review, various categories are adopted such as publication year, journals, research design, methodology, related research area, findings, limitations and challenges to the telecom industries. The study is useful for academic researchers, managers and practitioners in the telecom field. The authors critically reviewed the papers and develop a framework to study the significant factors influencing retention in telecom domain. The recommendations also have been made to this review paper on factors influencing customer retention and the directions for future researches are indicated. The authors have found 64 sub factors affecting retention in telecom sector and then categorized these factors into significant factors such as service, support, brand, network coverage, pricing, and customer relation and regulation impact.

/pdf/factors-affecting-customer-retention-in-telecom-sector-a-366wrozpx8.pdf

Factors Affecting Customer Retention in Telecom Sector: A systematic review in Indian perspective


 Background: With the vast heterogeneity of teaching methods, there remains a dilemma on what works best for teaching medical students. Keeping this in mind, we have conducted a qualitative study on flipped classroom technique-an active learning method. Active teaching learning strategies are learner oriented, helps students in application, analysis, evaluation and synthesis of knowledge.Methods: This study was conducted at a Tertiary care hospital in North India over a period of 2 months, amongst 2nd year undergraduate medical students attending classes in the Department of Obstetrics and Gynaecology. In a weekly class for 25 students, 6-7 were randomly selected for active learning sessions. Every week a different set of students were taken. 50 students from a class of 150 were enrolled for the sessions.The initial 60 minutes was given to read the assigned topics, in the next 60 minutes relevant discussion on clinical aspects and application was done and a feedback form was filled by the students. It consisted of questions on usefulness of the session, content and overall performance based on four level rubric grading system. Data was entered into Microsoft Excel sheet and analyzed on the basis of frequency distribution.Results: 29(58%) students found the method excellent, 18(36%) were also receptive towards it (scoring it 7 to 8 in terms of usefulness). None of the students found this method to be bad for learning and retaining. 32(64%) students found it excellent in terms content and teaching, 15(30%) students found it good with respect to content and 14(28%) with respect to teaching. Few students (6% and 8%) found it average in both aspects. Overall performances indicate that 68% students scored it as excellent; 24% as good and 8% as average. At the end of the session, 72% students felt an increase in confidence levels; 20% were unsure whether they had benefitted and 8% did not find any increase in confidence levels. Majority of students (76%) found these classes more interactive and easy to comprehend.Conclusions: Active teaching learning based on Flipped classroom pattern finds good satisfaction amongst students.

Effectiveness of Active Learning Strategy on Learning of Undergraduate Medical Students

Asian Case Research JournalVol. 27, No. 01, pp. 59-77 (2023) No AccessColdman Logistics: COVID-19 Vaccine for One and AllRajkumari Mittal, Parul Sinha, and Bikramjit RishiRajkumari MittalLal Bahadur Shastri Institute of Management, New Delhi, India, Parul SinhaSchool of Management, GD Goenka University, Gurugram, Haryana, IndiaE-mail Address: [email protected]Correspondence to Associate Professor Parul Sinha, School of Management, GD Goenka University, Sohna, Gurugram 122103, Haryana, India., and Bikramjit RishiSchool of Management & Entrepreneurship (SME), Shiv Nadar Institution of Eminence (SNIoE - Deemed to be University) Delhi - National Capital Region, Indiahttps://doi.org/10.1142/S0218927523500049Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail References 1. M. A. Andrews, B. Areekal, K. R. Rajesh, J. Krishnan, R. Suryakala, B. Krishnan, C. P. Muraly and P. Santhosh, “ First confirmed case of Covid 19 infection in India: A case report”, The Indian Journal of Medical Research, Vol. 151, No. 5 (2020), pp. 490-492, https://doi.org/10.4103/ijmr.IJMR_2131_20. Google Scholar2. ET News, “One year since a complete lockdown was announced, we look back on how India fought Covid”, The Economic Times, March 24, 2021, accessed March 4, 2022, https://economictimes.indiatimes.com/news/india/one-year-since-a-complete-lockdown-was-announced-we-look-back-on-how-india-fought-covid/first-lockdown-announced/slideshow/81662838.cms. Google Scholar3. Ministry of Health and Family Welfare, Government of India, “Latest updates”, 2021, accessed March 4, 2022, https://www.mohfw.gov.in/. Google Scholar4. S. Mordani, “Plan cold storage and supply chain for Covid vaccines by October 15, centre tells states as India toll hits 1 lakh”, News 18 India, October 3, 2020, accessed, https://www.news18.com/news/india/plan-cold-storage-and-supply-chain-for-covid-vaccines-by-oct-15-centre-tells-states-as-india-toll-hits-1-lakh-2929751.html. Google Scholar5. C. Kumar, “At 1.6 billion doses, India no. 1 in deals for Covid vaccine: Study”, The Times of India, December 4, 2020, accessed, https://timesofindia.indiatimes.com/india/at-1-6-billion-doses-india-no-1-in-deals-for-covid-vaccine-study/articleshow/79555922.cms. Google Scholar6. P. W. Stonebraker and R. Afifi, “ Toward a contingency theory of supply chains”, Management Decision, Vol. 42 No. 9 (2004), pp. 1131–1144, https://doi.org/10.1108/00251740410565163. Google Scholar7. Y. Taneja, “How to start a logistics business in India: Logistics company in India”, July 23, 2021, accessed, https://startuptalky.com/start-your-own-logistics-company-2020/#::̃text=A%20logistics%20company%20will%20manage,their%20trucks%20for%20interstate%20deliveries. Google Scholar8. Indian Chamber of Commerce, “Effects of corona virus outbreak in the Indian logistics & shipping industry & recommendations”, 2020, accessed March 4, 2022, https://www.indianchamber.org/wp-content/uploads/2020/04/ICC-Logistics-Sector-Update.pdf. Google Scholar9. Jean-Paul Rodrigue and Theo Notteboom, “The cold chain and its logistics”, The Geography of Transport Systems, 2017, accessed, https://transportgeography.org/contents/applications/cold-chain-logistics/. Google Scholar10. Mordor Intelligence, “India cold chain logistics market — Growth, trends, COVID-19 impact, and forecasts (2021-2026)”, 2021, accessed, https://www.mordorintelligence.com/industry-reports/india-cold-chain-logistics-market. Google Scholar11. National Centre for Cold-chain Development, “Welcome to National Centre for Cold-chain Development”, 2021, accessed March 4, 2022, https://www.nccd.gov.in/. Google Scholar12. Ministry of Food Processing Industry, “Pradhan Mantri Kisan Sampada Yojana”, 2021, accessed March 4, 2022, https://www.sampada-mofpi.gov.in/. Google Scholar13. Ministry of Food Processing Industry, “Cold chain”, 2021, accessed March 4, 2022, https://www.mofpi.gov.in/Schemes/cold-chain. Google Scholar14. Business Wire, “India cold chain logistics market report 2022: Featuring key players Snowman Logistics, Gati Kausar, ColdEx Logistics & Others — ResearchAndMarkets.com”, July 11, 2022, accessed September 17, 2022, https://www.businesswire.com/news/home/20220711005428/en/India-Cold-Chain-Logistics-Market-Report-2022-Featuring-Key-Players-Snowman-Logistics-Gati-Kausar-ColdEx-Logistics-Others—ResearchAndMarkets.com. Google Scholar15. K. Keja, C. Chan, G. Hayden and R. H. Henderson, “ Expanded programme on immunization”, World Health Statistics Quarterly, Vol. 41, No. 2 (1988), pp. 59–63, https://pubmed.ncbi.nlm.nih.gov/3176515/. Google Scholar16. National Health Portal (NHP), “Mission Indradhanush”, 2018, accessed March 4, 2022, https://www.nhp.gov.in/mission-indradhanush1_pg. Google Scholar17. National Health Portal (NHP), “Universal Immunisation Programme”, 2018, accessed March 4, 2022, https://www.nhp.gov.in/universal-immunisation-programme_pg. Google Scholar18. P. Kohli, “The saga of cold-chain development in India”, preprint, 2020, accessed, doi:10.13140/RG.2.2.13678.28489; Central Coordinating Office, “In-depth analysis of cold chain, vaccine supply and logistics management for routine immunization in three Indian states: An INCLEN program evaluation network study”, 2020, accessed, http://inclentrust.org/inclen/wp-content/uploads/Cold-Chain-Full-Report.pdf; C. Lahariya, “A brief history of vaccines & vaccination in India”. The Indian Journal of Medical Research, Vol. 139, No. 4 (2014), pp. 491–511. Google Scholar19. The Hindu, “Coronavirus: One in five Indians may be vaccinated by July 2021, says Harsh Vardhan”, October 4, 2020, accessed March 4, 2022, https://www.thehindu.com/news/national/coronavirus-up-to-25-crore-indians-expected-to-be-inoculated-for-covid-19-by-july-2021-says-health-minister/article32765273.ece. Google Scholar20. A. Bhuyan, “India’s Covid-19 dilemma: Adults need vaccines and supply chains geared for children”, IndiaSpend, October 13, 2020, accessed, https://www.indiaspend.com/indias-covid-19-dilemma-adults-need-vaccines-supply-chains-geared-for-children/. Google Scholar21. A. Gohel, “Covid-19 vaccine: All you need to know about manufacturing and distribution in India”, CNBCTV18, November 20, 2020, accessed, https://www.cnbctv18.com/healthcare/covid-19-vaccine-all-you-need-to-know-about-manufacturing-and-distribution-in-india-7520911.htm. Google Scholar22. Press Information Bureau, Government of India, “PIB’s special webpage on COVID19”, 2020, accessed March 4, 2022, https://pib.gov.in/newsite/coronaviruss.aspx. Google Scholar23. J. Manners-Bell and K. Lyon, The Logistics and Supply Chain Innovation Handbook: Disruptive Technologies and New Business Models, 1st Ed. (Hoboken, NJ: John Wiley & Sons, 2003); P. R. Murphy, Jr. and M. A. Knemeyer, Contemporary Logistics, 12th Ed. (New York, NY: Pearson Education, 2018); World Health Organization (WHO), “SAGE Working Group on Covid-19 vaccines (established June 2020)”, 2020, accessed, https://www.who.int/immunization/sage/sage_wg_covid-19/en/. Google Scholar Remember to check out the Most Cited Articles! Check out our Popular Science Titles today! Includes all-time bestsellers and new releases. FiguresReferencesRelatedDetails Recommended Vol. 27, No. 01 Metrics History Published: 10 March 2023 PDF download 

Parul Sinha

Papers

Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM

Factors Affecting Customer Retention in Telecom Sector: A systematic review in Indian perspective

Effectiveness of Active Learning Strategy on Learning of Undergraduate Medical Students

Coldman Logistics: COVID-19 Vaccine for One and All