Machine-Learning in Dementia Informatics Research- TutorsIndia.com

MACHINE-LEARNING IN DEMENTIA INFORMATICS RESEARCH An Academic presentation by Dr. Nancy Agens, Head, Technical Operations, Tutors India Group  www.tutorsindia.com Email: info@tutorsindia.com

Today's Discussion OUTLINE In Brief Introduction Methods Developing a ML-based Model to Identify Dementia Machine-Learning Models Limitations Conclusion

In Brief Machine Learning (ML) is the modern method used to predict, recognize, and to assess the disease correctly without the participation of humankind. ML is emerging rapidly in the field of medicine to diagnose the disease, to visualize the disease, and to examine the transmission of disease. Dementia is a chronic disease that affects millions of people worldwide.

Machine learning is used in neuro-imaging data analysis for the treatment of dementia patients.

Introduction The diagnosis usually includes set of clinical tests like cognitive assessments, history of patients, and neuro-imaging.

This is a very time-consuming process and also quite expensive.

Due to the progress and development in the technologies, to advance in information technology, the field of medical sciences has created huge set of data related to this disease.

The main objective is to create a model based on machine-learning that can be used to predict Alzheimer’s disease, cognitive impairment, and associated diseases of dementia.

Methods A methodology was used to detect dementia patients who are not diagnosed.

This is done with the help of read-encoded data that are collected regularly in primary care.

The methodology is as follows A list of Read codes that are related to the disease was gathered and this data was used to detect dementia patients.

The dataset was investigated to explore other Read codes that were allotted to the patients who have dementia.

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A division of Read codes was then established that has an association with dementia patients.

This division of codes are said to be the read-encoded risk factors associated with dementia patients.

The dataset that was obtained was then used to build a model based on machinelearning to identify dementia patients.

The proposed model was then tested and assessed for performance.

The status of dementia patients that have been predicted by the model was then evaluated further.

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Figure 1: Overview of Methodology

Developing a ML-based Model to Identify Dementia: A classifier was derived using Machine-learning that was used to characterize dementia patients, and this classifier can also be used to detect all possible cases associated with the disease.

The Read codes are used for building a dementia classification model.

The dataset guides the classifier derived using machine-learning to distinguish the patients who have dementia and those who are healthy.

These classifiers would be influenced to recognize healthy patients.

Machine-Learning Models

Support Vector Machine (SVM) is a machine-learning model and is widely used for pattern recognition and diagnosing the problems of dementia from data.

The NaĂŻve Bayes (NB) classifier is a machine-learning approach that can be used to acquire probabilistic knowledge to categorize unseen data.

Random Forest (RF) is an algorithm based on machine-learning that uses data to construct decision trees (DTs), and organize unseen data by merging each decision trees.

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Logistic Regression (LR) is an easy machine-learning model that has been mostly used in binary classification problems and for early detection of the disease.

There are four criterions that can be used to examine the performance of the machine-learning classification: specificity, accuracy, sensitivity, and area under the curve (AUC).

After this preliminary assessment, the model was checked on the primary care dataset to decide undiagnosed dementia patients.

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Figure 2: Number of Potentially Undiagnosed Dementia Cases

Limitations Dementia is one of the ill health problems that have been a great challenge for the health experts worldwide.

Additionally, the disease affects mostly older people above age of 60.

This disease is worse that it can even cause damage to brain and reduces the patient’s ability to do their activities.

Still, the research is carried on to find a cure for this disease from the past 2 decades

Conclusion The healthcare practitioner determines the accuracy and efficiency of the diagnosis.

As there is a lack of practitioners in some areas, it is even more difficult for the diagnosis of the disease.

Machine-Learning helps in the progress of the analysis of medical data, and automatically make the decision for diagnosis.

The machine-learning field has become very active recently that it uses variety of patient’s data to discover new biomarkers for diagnosis and to improve the diagnostic ability.

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