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My Ultimate That Girl Goals 🥰🎯

forget my home, my entire life needs some spring cleaning 🌼🌱🧼

This time I've really gotten swept up in the tide of the semester and my responsibilities at work that I've let myself fall to the wayside, but no longer! 😤 Inside, I will always be that girl whom I can be proud of, but it's time to start living like I love me again 💕

I'm also not going to be setting a timeframe for this challenge because whenever I do that, I go all in for the challenge period, and then just feel too tired to maintain it regularly 🙈 So this is the ultimate, all-time challenge! 💪

🌄 Be a morning person again

meditate right after waking up

make the bed right after that

🏋️‍♀️ Be obsessed with working out again

actually take my physiotherapy exercises seriously

actually make time in my day for working out

🎨 Be in-tune with my creativity again

dedicate time for creative writing

dedicate time for sketching

✨ Be unafraid to take it up a notch again

actually read recreationally for 30 minutes daily

do 6 language lessons daily

💌: aaaah i'm nervous, but excited!! i believe it's possible! it'll just take some time and effort that i am willing to put into myself ❤️

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Predicting Alzheimer's With Machine Learning

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis is crucial for managing the disease and potentially slowing its progression. My interest in this area is deeply personal. My great grandmother, Bonnie, passed away from Alzheimer's in 2000, and my grandmother, Jonette, who is Bonnie's daughter, is currently exhibiting symptoms of the disease. This personal connection has motivated me to apply my skills as a data scientist to contribute to the ongoing research in Alzheimer's disease.

Model Creation

The first step in creating the model was to identify relevant features that could potentially influence the onset of Alzheimer's disease. After careful consideration, I chose the following features: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Socioeconomic Status (SES), and Normalized Whole Brain Volume (nWBV).

MMSE: This is a commonly used test for cognitive function and mental status. Lower scores on the MMSE can indicate severe cognitive impairment, a common symptom of Alzheimer's.

CDR: This is a numeric scale used to quantify the severity of symptoms of dementia. A higher CDR score can indicate more severe dementia.

SES: Socioeconomic status has been found to influence health outcomes, including cognitive function and dementia.

nWBV: This represents the volume of the brain, adjusted for head size. A decrease in nWBV can be indicative of brain atrophy, a common symptom of Alzheimer's.

After selecting these features, I used a combination of Logistic Regression and Random Forest Classifier models in a Stacking Classifier to predict the onset of Alzheimer's disease. The model was trained on a dataset with these selected features and then tested on a separate dataset to evaluate its performance.

Model Performance

To validate the model's performance, I used a ROC curve plot (below), as well as a cross-validation accuracy scoring mechanism.

The ROC curve (Receiver Operating Characteristic curve) is a plot that illustrates the diagnostic ability of a model as its discrimination threshold is varied. It is great for visualizing the accuracy of binary classification models. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.

The area under the ROC curve, often referred to as the AUC (Area Under the Curve), provides a measure of the model's ability to distinguish between positive and negative classes. The AUC can be interpreted as the probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.

The AUC value ranges from 0 to 1. An AUC of 0.5 suggests no discrimination (i.e., the model has no ability to distinguish between positive and negative classes), 1 represents perfect discrimination (i.e., the model has perfect ability to distinguish between positive and negative classes), and 0 represents total misclassification.

The model's score of an AUC of 0.98 is excellent. It suggests that the model has a very high ability to distinguish between positive and negative classes.

The model also performed extremely well in another test, which showed the model has a final cross-validation score of 0.953. This high score indicates that the model was able to accurately predict the onset of Alzheimer's disease based on the selected features.

However, it's important to note that while this model can be a useful tool for predicting Alzheimer's disease, it should not be the sole basis for a diagnosis. Doctors should consider all aspects of diagnostic information when making a diagnosis.

Conclusion

The development and application of machine learning models like this one are revolutionizing the medical field. They offer the potential for early diagnosis of neurodegenerative diseases like Alzheimer's, which can significantly improve patient outcomes. However, these models are tools to assist healthcare professionals, not replace them. The human element in medicine, including a comprehensive understanding of the patient's health history and symptoms, remains crucial.

Despite the challenges, the potential of machine learning models in improving early diagnosis leaves me and my family hopeful. As we continue to advance in technology and research, we move closer to a world where diseases like Alzheimer's can be effectively managed, and hopefully, one day, cured.

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Understanding IHD with Data Science

Ischemic Heart Disease (IHD), more commonly recognized as coronary artery disease, is a profound health concern that stems from a decreased blood supply to the heart. Such a decrease is typically due to fatty deposits or plaques narrowing the coronary arteries. These arteries, as vital conduits delivering oxygen-rich blood to the heart, play a paramount role in ensuring the heart's efficient functioning. An obstruction or reduced flow within these arteries can usher in adverse outcomes, with heart attacks being the most dire. Given the gravity of IHD, the global medical community emphasizes the essence of early detection and prompt intervention to manage its repercussions effectively.

A New Age in Healthcare: Embracing Data Science

As we stand on the cusp of the fourth industrial revolution, technology's intertwining with every domain is evident. The healthcare sector is no exception. The integration of data science in healthcare is not merely an augmentation; it's a paradigm shift. Data science, with its vast array of tools and methodologies, is fostering new avenues to understand, diagnose, and even predict various health conditions long before they manifest pronounced symptoms.

Machine Learning: The Vanguard of Modern Medical Research

Among the myriad of tools under the vast umbrella of data science, Machine Learning (ML) shines exceptionally bright. An essential offshoot of artificial intelligence, ML capitalizes on algorithms and statistical models, granting computers the capability to process vast amounts of data and discern patterns without being explicitly programmed.

In the healthcare realm, the applications of ML are manifold. From predicting potential disease outbreaks based on global health data trends to optimizing patient flow in bustling hospitals, ML is progressively becoming a linchpin in medical operations. One of its most lauded applications, however, is its prowess in early disease prediction, and IHD detection stands as a testament to this.

Drawn to the immense potential ML holds, I ventured into a research project aimed at harnessing the RandomForestClassifier model's capabilities. Within the medical research sphere, this model is celebrated for its robustness and adaptability, making it a prime choice for my endeavor.

Deep Dive into the Findings

The results from the ML model were heartening. With an accuracy rate of 90%, the model’s prowess in discerning the presence of IHD based on an array of parameters was evident. Such a high accuracy rate is pivotal, considering the stakes at hand – the very health of a human heart. 9 times out of 10 the model is correct at its predictions.

Breaking down the data, some correlations with IHD stood out prominently:

Moderate COPD (Chronic Obstructive Pulmonary Disease) – 15%: COPD's inclusion is noteworthy. While primarily a lung condition, its linkage with heart health has been a topic of numerous studies. A compromised respiratory system can inadvertently strain the heart, underscoring the interconnectedness of our bodily systems.

Diabetes – 18%: The correlation between diabetes and heart health isn't novel. Elevated blood sugar levels over extended periods can damage blood vessels, including the coronary arteries.

Age (segmented in quarterlies) – 15%: Age, as an immutable factor, plays a significant role. With age, several bodily systems gradually wear down, rendering individuals more susceptible to a plethora of conditions, IHD included.

Smoking habits – 14%: The deleterious effects of smoking on lung health are well-documented. However, its impact extends to the cardiovascular system, with nicotine and other chemicals adversely affecting heart functions.

MWT1 and MWT2 (indicators of physical endurance) – 13% and 14% respectively: Physical endurance and heart health share an intimate bond. These metrics, gauging one's physical stamina, can be precursors to potential heart-related anomalies.

Redefining Patient Care in the Machine Learning Era

Armed with these insights, healthcare can transcend its conventional boundaries. A deeper understanding of IHD's contributors empowers medical professionals to devise comprehensive care strategies that are both preventive and curative.

Moreover, the revelations from this study underscore the potential for proactive medical interventions. Instead of being reactive, waiting for symptoms to manifest, healthcare providers can now adopt a preventive stance. Patients exhibiting the highlighted risk factors can be placed under more meticulous observation, ensuring that potential IHD developments are nipped in the bud.

With the infusion of machine learning, healthcare is on the cusp of a personalized revolution. Gone are the days of one-size-fits-all medical approaches. Recognizing the uniqueness of each patient's health profile, machine learning models like the one employed in this study can pave the way for hyper-personalized care regimens.

As machine learning continues to entrench itself in healthcare, a future where disease predictions are accurate, interventions are timely, and patient care is unparalleled isn't merely a vision; it's an impending reality.

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Studying Korean is aesthetic 🩷

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Crochet lamp 🪔

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Crochet is all over fashion again this spring. Reminder that crochet cannot be done by machine, so someone had to make it by hand. There is literally no fast fashion brand that is paying a fair wage to the artisans who are doing that work, even taking local wages in other countries into account. And you can tell that by the pricing. I crochet faster than most people I know, and a jacket always takes me at least 20 hours. And dresses take 30-50. The smaller the yarn, the more hours it'll take to make something.

There are tons of crocheters on Etsy setting their own prices. Check there before you shop Target or Express or any other place selling on a rack.

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Finally got my hands on a crochet hook and yarn, this is gonna become my whole personality rn

#me 3 days ago. I’m so excited. I’m gonna make squares and little guys

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What your favorite fiber art says about you:

Knitting: you like the classics. Pride and Prejudice is your favorite book and you prefer the BBC miniseries to the 2005 movie.

Crochet: you are either a current or former weeb. Either way, amigurumi was the gateway drug.

Embroidery: You have been on tumbler.com since 2011 and think feminism is just embroidering a pair of titties on a tea cozy and selling it on Etsy for $55.72

Cross-stitch: oh, hi there grandma 😄

Quilting: a serene, motherly person. You ARE the cottagecore aesthetic personified.

Needle Felting: you either have a ton of unrelieved tension or you just love the masochism of a craft that can draw blood

Sewing: whether it’s cosplay or historical recreations, you’re a huge f*cking nerd and I respect the hell out of that.

Tatting: you started out crocheting doilies but then thought that was too mainstream. You enjoy when people at the coffee shop ask you what book you’re reading.

Macramé: you live on pinterest, got married at 22, have balayaged hair, and wear those wide brimmed hats with infinity scarves and riding boots.

Bobbin Lace: you are God.

Weaving: you a village wise woman or you are a spider

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helppp i’m super super new to crocheting, i can confidently do the chain stitch and single crochet stitch but that’s about it so far… i wanna learn the other essential stitches as well of course but like, i think i want to start on my first project?

but i don’t know what would be suitable for a COMPLETE beginner like me :< i thought about granny squares maybe? but they use so many different stitches and i get super frustrated super easily :((

so yes in short, if anyone out there has some recommendations as to what a crochet newbie with zero projects under their belt could do as their first, let me know!!!

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Crochet a Mini Puzzle Bag With This Free Tutorial! 👉 https://buff.ly/3L6XmVu 🌼

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Tiny Teddy Bear by Nicole Riley

Free Crochet Pattern Here

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Bunny Rabbit by Skein Spider

Free Crochet Pattern Here *** Video Pattern Only ***

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the feeling of learning is legitimately so cool. there’s a little whoa sound effect that plays in your brain whenever you read a sentence that expands reality for you a tiny bit

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Technically all you have to do to learn a language is drill flash cards and watch tv but the problem is the amount of flash cards and tv and what they don’t tell you is that your brain will protest the entire time

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UGH is is literally so much to ask that I have time and money to learn how to tailor clothes and embroider and knit and make cakes and bake bread and do makeup and hair and nails and do all my own diy projects around the house and

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By the way, since there appear to be a few posts going around right now about speaking Spanish, I want to encourage other Americans to learn it. We have the…dubious…distinction of being one of the few countries in the Western world where it’s not normal to learn a minimum of two languages, and although some schools are starting to get with the program we still lag far behind.

There are a billion thinkpieces by white authors about how Spanish gives you a business advantage. Fuck ‘em, you know that. There are studies showing that learning a second language can help fend off Alzheimer’s and make it easier to recover from a stroke. Nice, but irrelevant here.

What’s important is that Latines and Hispanics are the largest racial minority in the United States, and are on track to actually become a majority-minority by 2050. We need to be able to communicate and also, it’s always cool to share culture.

And before anyone gives me “they should learn English if they’re coming here,” one, I bet you’re one of those racist assholes who says you’re celebrating “Cinco de Drinko,” and two, I’ve never read a single textbook suggesting Iroquois or Arapahoe were part of the standard curriculum back in the 1600s, so I don’t wanna hear SHIT about English.

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dear non-spanish speakers writing spiderverse fanfiction (or anything with spanglish),

in spanglish you don’t switch by word, you switch by phrase.

it’s not:

“[first part of the sentence in english], [second part of the sentence in english], mi amor.”

“[full english sentence], querida.”

it’s:

“[first part of the sentence in english], [segunda parte de la frase en español], mi amor.”

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also miles is boricua, miguel is mexican. they have two different accents and use different vocabulary for certain words.

also miles is “nyourican” - a puerto rican native to new york - while his mom is directly from the island, so there are differences there, too, because his spanish is more influence by new york english. 

here’s some good references that aren’t google translate (which usually pulls from spain, a country that speaks vastly differently from latin america)

SpanishDict

WordReference

here have some random videos on different slang/spanish accents:

Puerto Rico

Mexico (1) (2)

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in spanish most words are gendered, so most feminine words end in a and masculine/gender neutral words end in o. adding ito/ita makes something cuter, smaller and more affectionate.

spanish nicknames that aren’t “mi amor”

“querido/a” - darling

“cariño” - dear (always masculine regardless, of who its being said to)

“mi princesa/príncipe” - my prince/princess

“mi rey/reina” - my king/queen

“papí/mamí” - can be used in any way; romantic, sexual, familial for one’s parent or child, or just platonically

“tesoro” - treasure

also spanish is a language that uses adjectives as terms of affection both cute ones and ones that might sound insensitive in english

gordo (fat), flaco (skinny), negro (black), blanco (white), linda (pretty), bella (beautiful), morena (brown skin), etc.

and like most languages that are not english, spanish has multiple ways of saying i love you.

“te amo” - romantic

“te quiero” - familial, platonic (although there’s nothing wrong with using it romantically)

see also:

te adoro - i adore you

te deseo - i want you

te necesito - i need you

 and, of course, they can vary regionally too.

please use this because i have read a lot of really well written things that take me out of it because the use of spanglish is terrible. don’t just go on your presumptions that spanish/spanglish works in the same way that english does.

buena suerte, gringos.

- signed your friendly neighborhood afro-latina