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Authors | Tool/technology | Methodology | Purpose of finding | Strength | Weakness |
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Dimitrov [5] | (i) Sensing technology | Emergence of medical internet of things (mIoT) in existing mobile apps | Providing benefits to the customers | (i) Achieving improved mental health | Adding up garbage data to the sensors |
(ii) Artificial intelligence | (i) Avoiding chronic and diet-related illness |
(ii) improving cognitive function | (ii) improving lifestyles in real time decision-making |
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Monteith et al. [6] | Survey based approach | Clinical data mining | Analyzing different data sources to get psychiatry data | Optimized precedence opportunities for psychiatry | N/A |
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Kellmeyer [7] | Neurotechnology | (i) Machine learning | Enhancing the security of devices and sheltering the privacy of personal brain | Maximizing medical knowledge | Model needs huge amount of training data as brain disease is rarely captured |
(ii) Consumer-directed neurotechnological devices |
(iii) Combining expert with a bottom-up process |
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Yang et al. [9] | Long-term monitoring wearable device with internet of things | Well-being questionnaires with a group of students | Developing app-based devices linked to android phones and servers for data visualization monitoring and environment sensing | Perfectly working on long-term data | Offline data transfer instead of real time |
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Monteith and Glenn [10] | Automated decision-making | Hybrid algorithm that combines the statistical focus and data mining | Tracking day-to-day behavior of the user by automatic decision-making | Automatically detecting human decision without any input | How to ignore irrelevant information is a key headache |
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De Beurs et al. [14] | Online intervention | Expert-driven method Intervention mapping Scrum | Increasing user involvement under limited resources | Standardizing the level of user involvement in the web-based healthcare system | Deciding threshold for user involvement is problematic |
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Kumar and Bala [12] | Hadoop | Doing sentimental analysis and saving data on Hadoop | Analyzing twitter users’ view on a particular business product | Checking out popularity of a particular service | Usage of two programming languages needs experts |
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Goyal [11] | KNN and Naïve Bayes classifier | Text mining and hybrid approach combining KNN and Naïve Bayes | Opinion mining of tweets related to food price crisis | Cost-effective way to predict prizes | Data needs to be cleaned before training |
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