MACHINE-LEARNINGIN DEMENTIAINFORMATICSRESEARCH

An Academic presentation by Dr. Nancy Agens, Head, Technical Operations, Tutors India Group  www.tutorsindia.comEmail: info@tutorsindia.com

In BriefIntroductionMethodsDeveloping a ML-based Model to Identify DementiaMachine-Learning ModelsLimitationsConclusion

OUTLINE

Today's Discussion

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 fieldof medicine to diagnose the disease, to visualize the disease,and to examine the transmission of disease. Dementia is achronic disease that affects millions of people worldwide.

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

The diagnosis usually includes set of clinical tests like cognitive assessments, history ofpatients, 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 informationtechnology, 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 topredict Alzheimer’s disease, cognitive impairment, and associated diseases of dementia.

Introduction

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.

A list of Read codes that are related to the disease was gathered and this data was used todetect dementia patients.

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

The methodology is as follows

Methods

Contd..

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

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

The dataset that was obtained was then used to build a model based on machine-learning 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 thenevaluated further.

Contd..

Figure 1: Overviewof Methodology

A classifier was derived using Machine-learning that was used to characterizedementia patients, and this classifier can also be used to detect all possible casesassociated 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 thepatients who have dementia and those who are healthy.

These classifiers would be influenced to recognize healthy patients.

Developing a ML-based Modelto Identify Dementia:

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

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

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

Machine-Learning Models

Contd..

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

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

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

Contd..

Figure 2: Numberof PotentiallyUndiagnosedDementia Cases

Dementia is one of the ill health problems that have been a great challenge for thehealth 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 thepatient’s ability to do their activities.

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

Limitations

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 diagnosisof the disease.

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

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

Conclusion

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