# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
### Potential Role of Machine Learning  
The early prognosis of dementia relies more on fine-tuned data marks than biomarkers (a naturally occurring molecule, gene, or characteristic by which a particular pathological or physiological process, disease, etc. can be identified). As mentioned earlier, initial identification and isolation of dementia are carried out using quantifiable tests and subjective tests which are ripe candidates for machine learning models [5]. With historical data available, an analytical and machine learning-based approach could be a promising tool for the prognosis of dementia. Among varied approaches, machine learning has the potential to facilitate a unified model of cognitive assessment based on historic data. This facilitates the conversion of this human-intensive activity into an automated process that reliably communicates trends to health care professionals. It is an important point when we consider that the ratio of health care providers to people who need health care is dismal. The ideal ratio of psychiatrists to the population is about 1:8000 to 10,000, but in low- to middle-income countries, it can be as low as 1 psychiatist serving over 200,000 or more people [6].  
[PERSON] et al. [7] applied machine learning to deduce the onset of dementia. The data source was a de-identified national dataset of more than 125 million patients aged above 65 and their 10,000 clinical, pharmaceutical, and demographic variables (contrasted with lifestyle parameters in our work). This research aimed to identify a cohort of features from the dataset that have been proven significant in the development of a machine learning model to predict Alzheimer's disease and related dementias (ADRD) 4-5 years in advance. They employed lasso regression and random forest (RF) to filter and shortlist significant parameters that had a significant impact on the onset of dementia. They shortlisted 50 features and developed a machine learning model using logistic regression to predict whether a given patient is at risk of ADRD or not. [PERSON], [PERSON], et al. [8] used prescription-based data to identify risk markers for dementia. Their study used the UK Clinical Practice Research Datalink (CPRD). This data set contains anonymized health care records of around 20 million people. The major features of data include medical diagnoses, primary and secondary referrals, and prescriptions as well as lifestyle information. The data is encoded in a multilevel vocabulary system referred to as'read codes'. Every read code signifies indicators of a health-related concept. There are more than 200,000 read codes classified into different chapters. Read codes are represented by a 5-byte alphanumeric code.  
The creators of the system used logistic regression with lasso, naive Bayes classifier, support vector machine, random forest, and neural networking to classify subjects into cemented or non-demented. Naive Bayes performed the least well, and the logistic regression method performed the best with an AUROC (area under the receiver operating characteristics) of 0.74. The top features that correlated with dementia were wandering and disorientation. [PERSON] et al. [9] investigated cases of undiagnosed dementia in primary care. They used publicly available National Health Service (NHS) data. The objective of this study was to develop a machine learning model that can be used to detect dementia using routinely collected health data from the NHS. The model is useful in the detection of patients who may have dementia but have not been diagnosed with it. Input data were routinely collected in the form of'read encoded data' from 18 consenting GPs for 26,483 patients aged above 65 years. ML techniques used were SVM, naive Bayes, random forest, and logistic regression. The study outcome was a binary variable indicating diagnosis.