A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.

Intelligent Healthcare System for Recuperation and Inoculation of Communicable Diseases

Human life gets interrupted a lot when a communicable disease starts in a society. People are alerted a lot on a different basis and eventually, it leads to difficulty for continuing their normal life. This paper is focused on a communicable disease, the COVID-19, which gave an outbreak for all world counties into a state where their fellow human had to stop their daily job and stay back at home. Common people find it difficult to get treatment when they are identified as positive and also most of them lack the awareness of post covid recovery. The idea of this work is to create a healthcare system that initiates right from the covid test, appropriate treatment according to severity, post-recovery, and vaccination awareness and jabs. These units will have less human interaction and they will be fully automated till in need of pure doctor consultation. The automated system is programmed to identify the infection, its severity and prescribe the treatment accordingly, and follow up recovery needs. On-side, automated vaccination system will allow direct walk-in, identify the customer and do the needful.

Treatment Case Studies and Emissions Analysis of Wood in Yagya

The mantra becomes more powerful when that sound is chanted in front of purified fire and light; sound and heat energy mixtures are converted into high level of energy and spread around the atmosphere. Through this paper, the well experienced author team of various domains is continuously working in experimenting in joint collaboration with different GoI departments. They have observed the slow but continuous progress in different ails on many subjects through scientific study and approach. The main case studies where the patients got significant benefits through this alternate therapy have been systematically presented here. The power of yajna and mantra has attracted the intellectuals of this era. In the future, we may expect some automated intelligent healthcare expert system using this way of life. Yagya science and its treatment power of different diseases is surprising; the need is that current science should accept it logically with an open mind and heart and let the humanity take the complete benefit of it.