scholarly journals An Advanced Machine Learning Model for Disease Prediction

2020 ◽  
Vol 9 (2) ◽  
pp. 1220-1225
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Model ◽  
Control Measures ◽  
Disease Prediction ◽  
Common People ◽  
Machine Learning Model ◽  
The World ◽  
Conventional Models ◽  
Precision Rate

To settle on right choices and pass on about vital control measures, numerous flare-up expectation models for anticipating COVID-19 are getting utilized all round the world. Straightforward conventional models have indicated extremely less precision rate for future forecast use, because of more significant levels of vulnerability and absence of proper information. Among the different machine learning model algorithms contemplated, an ensembled model was seen as giving the best outcomes. Because of the multifaceted nature of the virus's temperament, this research paper recommends machine learning to be an extremely helpful gadget to consider in case of the ongoing pandemic. This paper gives a colossal benchmark to call attention to the probability of machine learning to be utilized as an instrument for future exploration on pandemic control and its timely prediction. Moreover, this paper delineates that the best prompts for pandemic prediction are frequently comprehended by combining machine learning, predictive analytics and visualisation tools like Tableau. The main purpose of this research is to build a perfect ML model prototype which can be later used when access to appropriate dataset (which is both large and consists of many different features) is available. Also, the secondary aim is to automate the process of reporting so as to facilitate quicker action by the concerned authorities, and help common people reach out to the correct destination for treatment or help. Furthermore, the Tableau analysis performed on the dataset is to provide more analytical depths for people with expertise in the medical domain.

Computationally Efficient and Effective Machine Learning Model Using Time Series Data in Different Prediction Problems

2021 ◽  
pp. 218-233
Author(s):  
Dwiti Krishna Bebarta ◽  
Birendra Biswal
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Learning Model ◽  
Series Data ◽  
Computationally Efficient ◽  
Machine Learning Model ◽  
High Degree ◽  
Prediction Problems

Automated feature engineering is to build predictive models that are capable of transforming raw data into features, that is, creation of new features from existing ones on various datasets to create meaningful features and examining their effect on planned model performances on various parameters like accuracy, efficiency, and prevent data leakage. So the challenges for experts are to plan computationally efficient and effective machine, learning-based predictive models. This chapter will provide an imminent to the important intelligent techniques that could be utilized to enhance predictive analytics by using an advanced form of the predictive model. A computationally efficient and effective machine learning model using functional link artificial neural network (FLANN) is discussed to design for predicting the business needs with a high degree of accuracy for the traders or investors. The performance of the models using FLANN is encouraging when scientifically analyzed the experimental results of the model using different statistical analyses.

scholarly journals Covid-19 Analysis and Prediction using Data Science and Machine Learning

2021 ◽  
Vol 9 (12) ◽  
pp. 303-307
Author(s):  
Akshata Kulkarni
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Information Dissemination ◽  
Prediction Models ◽  
Learning Model ◽  
Future Trend ◽  
Control Measures ◽  
Machine Learning Model ◽  
Using Data ◽  
Novel Coronavirus

Abstract: Officials around the world are using several COVID-19 outbreak prediction models to make educated decisions and enact necessary control measures. In this study, we developed a Machine Learning model which predicts and forecasts the COVID-19 outbreak in India, with the goal of determining the best regression model for an in-depth examination of the novel coronavirus. Based on data available from January 31 to October 31, 2020, collected from Kaggle, this model predicts the number of confirmed cases in Maharashtra. We're using a Machine Learning model to foresee the future trend of these situations. The project has the potential to demonstrate the importance of information dissemination in improving response time and planning ahead of time to help reduce risk.

Predictive Analytics for OSA Detection Using Non-Conventional Metrics

2020 ◽  
Vol 10 (4) ◽  
pp. 13-23
Author(s):  
Vinit Kumar Gunjan ◽  
Madapuri Rudra Kumar
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Sleep Apnea ◽  
Early Diagnosis ◽  
Predictive Analytics ◽  
Learning Model ◽  
Model Tests ◽  
Svm Classifier ◽  
Machine Learning Model ◽  
Effective Performance

Early diagnosis in the case of the sleep apnea has its own set of benefits for treating the cases. However, there are many challenges and limitations that impact the current conditions for testing. In this manuscript, a model is proposed for early diagnosis of OSA, using the non-conventional metrics. Profoundly, the metrics used are combination of symptoms, causes, and effects of the problem. Using a machine learning model and two sets of classifiers, the inputs collected as part of the training datasets are used for analysis. The data classifiers used for the model tests are NB and SVM. In a comparative analysis of the results, it is imperative that SVM classifier-based training of the proposed algorithm is giving more effective performance.

scholarly journals Machine Learning model to predict the number of cases contaminated by COVID-19.

2020 ◽  
Author(s):  
Allae Erraissi ◽  
Mohamed Azouazi ◽  
Abdessamad Belangour ◽  
Mouad Banane
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Case Description ◽  
Corona Virus ◽  
Machine Learning Model ◽  
The World ◽  
Study Case

Abstract Introduction: This paper presents a dedicated machine learning model to predict the number of cases infected by the Corona Virus; the case of Morocco was chosen to validate this study. Case description: Completely realized in Spark ML with the 'Scala' language and tested for a certain number of algorithms generated on datasets coming from dedicated sources to gather Covid19 data in the world. Discussion and Evaluation: The results show the possibility of achieving better scores prediction after using the proposed method. We tested our model on the case of China and the results were relevant. Conclusion The proposed Machine Learning model can be applied to data from any country in the world. We have applied it in this paper to the case of Morocco and China. We are sending this work to the world to help them fight this 2019 Corona Virus pandemic.

A novel work on analyzing Stress and Depression level of Indian population during COVID-19

2020 ◽  
Vol 13 ◽  
Author(s):  
Amit Kumar Gupta ◽  
Priya Mathur ◽  
Shruti Bijawat ◽  
Abhishek Dadhich
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Age Groups ◽  
Multiple Linear Regression Model ◽  
Learning Model ◽  
The People ◽  
Machine Learning Model ◽  
The World ◽  
Depression Level ◽  
Detection Of Stress

Objective: The world is facing the pandemic situation of COVID-19 which leads to a large level of stress and depression on mankind as well on society. Static measurements can be conducted for early identification of the stress and depression level and diagnose or preventing from the effect of these conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression of humankind by statistically analyzing the behavior of humankind which helps to the early detection of stress and depression. This helps to prevent society from psychological pressures from any disaster like COVID-19. The COVID-19 pandemic is one of the public health emergencies which are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the disaster caused by this disease. Methods: In this paper, the authors have surveyed by defining some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the situation COVID19The data sets are analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression is mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The Machine learning model is the best way to predict the level of stress and depression of humankind by statistically analyzing the behavior of humankind which helps the early detection of stress and depression. Results: Females are more stressed and depressed than males. The people who are 45+ years age are more stressed and depressed. The male and female students are more stressed and depressed. The overall analysis said that the peoples of India are stressed and depressed at the level of “Serve” due to COVID-19. This can because of a student’s career concerning their study and examination. The females who feel so much burden of business as well as their salary. The aged people are depressed due to COVID-19 disaster. Conclusion: This research given very big support to understand our objectives. We have also implemented our analysis of data based on DASS-21 parameters defined for the Anxiety, Depression, and stress at the world level. By the analysis defined in section 5 we conclude that the people of India are more stressed and depressed at the level of "Serve" due to COVID-19.

An Effective and Efficient Alzheimer Disease Prediction System Using Machine Learning Model

2021 ◽  
Author(s):  
B.V Chowdary ◽  
Shrimukhi Muppidi ◽  
Bedapudi Sruthi ◽  
Kuntapally Sai Madhuri ◽  
L. Sumanth
Keyword(s):  
Machine Learning ◽  
Alzheimer Disease ◽  
Learning Model ◽  
Disease Prediction ◽  
Prediction System ◽  
Machine Learning Model
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